Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:01, 9.00MB/s]                            
Extracting mnist: 100%|██████████| 60.0k/60.0k [00:05<00:00, 11.6kFile/s]
Downloading celeba: 1.44GB [08:14, 2.92MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7ff5ab20ddd8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7ff5ab4e6400>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.8.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Functio
    real_input = tf.placeholder(tf.float32, shape=[None, image_height, image_height, image_channels], name='real_input')
    z_data = tf.placeholder(tf.float32, shape=[None, z_dim])
    learning_rate = tf.placeholder(tf.float32)

    return real_input, z_data, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        
        # Input is an image: 28x28x3(or 1)
        c1 = tf.layers.conv2d(images, 64, [5,5], [2,2], activation=None, padding='same')
        c1 = tf.maximum(0.2*c1, c1)
        
        # Input is 14x14x64
        c2 = tf.layers.conv2d(c1, 128, [5,5], [2,2], activation=None, padding='same')
        c2 = tf.layers.batch_normalization(c2, training=True)
        c2 = tf.maximum(0.2*c2, c2)
        
        # Input is 7x7x128
        c3 = tf.layers.conv2d(c2, 256, [5,5], [2,2], activation=None, padding='same')
        c3 = tf.layers.batch_normalization(c3, training=True)
        c3 = tf.maximum(0.2*c3, c3)
        
        # Input is 4x4x256
        c3_flat = tf.reshape(c3, [-1, 4*4*256])
        logits = tf.layers.dense(c3_flat, 1)
        output = tf.sigmoid(logits)
        
    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    reuse = not is_train
    with tf.variable_scope('generator', reuse=reuse):
        # First layer is fully connected.
        layer_size = 7*7*512
        x = tf.layers.dense(inputs=z, units=layer_size)

        # Reshape into a convolutional layer.
        c1 = tf.reshape(x, [-1, 7, 7, 512])
        c1 = tf.layers.batch_normalization(c1, training=is_train)
        c1 = tf.maximum(0.2*c1, c1)
        
        # Input is 7x7x512
        c2 = tf.layers.conv2d_transpose(c1, 256, 5, [2,2], activation=None, padding='same')
        c2 = tf.layers.batch_normalization(c2, training=is_train)
        c2 = tf.maximum(0.2*c2, c2)
        
        # Input is 14x14x256
        c3 = tf.layers.conv2d_transpose(c2, 128, 5, [2,2], activation=None, padding='same')
        c3 = tf.layers.batch_normalization(c3, training=is_train)
        c3 = tf.maximum(0.2*c3, c3)
        
        # Convert to 28x28x256 to 28x28xout_channel_dim
        logits = tf.layers.conv2d_transpose(c3, out_channel_dim, 5, [1,1], activation=None, padding='same')
        
        # Discriminator excepts -1 to 1 -> apply tanh -> -0.5 to 0.5 since images are scaled to this range.
        output = 0.5*tf.tanh(logits)
    
        return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, True)
    
    d_real_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_fake_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_real_loss + d_fake_loss
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """

    # Get weights and bias to update.
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
        
    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [17]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, freq_see_loss = 10):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    
    # Get the out_channel_dim.
    if data_image_mode == "RGB":
        out_channel_dim = 3
    elif data_image_mode == "L":
        out_channel_dim = 1
        
    # Get the image dimensions.
    image_width = data_shape[-2]
    image_height = data_shape[-3]
        
    # Get the model inputs.
    real_input, z_data, learning_rate_placeholder = model_inputs(image_width, image_height, out_channel_dim, z_dim)
    
    # Obtain the losses.
    d_loss, g_loss = model_loss(real_input, z_data, out_channel_dim)
    
    # Obtain the model optimization operations.
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    # Container to hold the losses.
    g_losses = []
    d_losses = []
    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps+=1
                
                # Generate the random z vector.
#                 z_data_input = tf.random_uniform([batch_size, z_dim], -1, 1)
                z_data_input = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                sess.run(d_train_opt, feed_dict={real_input:batch_images, z_data:z_data_input})
                sess.run(g_train_opt, feed_dict={real_input:batch_images, z_data:z_data_input})
                
                if steps % freq_see_loss == 0:
                    d_loss_out, g_loss_out = sess.run([d_loss, g_loss], feed_dict={real_input:batch_images, z_data:z_data_input})
                    
                    print('Epoch: {} -- Step: {} -- d_loss: {} -- g_loss: {}'.format(epoch_i, steps, d_loss_out, g_loss_out))
                    g_losses.append(g_loss_out)
                    d_losses.append(d_loss_out)
                
                if steps % 100 == 0:
                    z_data_input_tensor = tf.random_uniform([16, z_dim], -1, 1)
                    show_generator_output(sess, 16, z_data_input_tensor, out_channel_dim, data_image_mode)
                    
    pyplot.figure()
    pyplot.plot(g_losses)
    pyplot.title('g_losses')
    pyplot.figure()
    pyplot.plot(d_losses)
    pyplot.title('d_losses')
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [18]:
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))

with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch: 0 -- Step: 10 -- d_loss: 0.2919052541255951 -- g_loss: 4.940899848937988
Epoch: 0 -- Step: 20 -- d_loss: 0.022066690027713776 -- g_loss: 4.52243185043335
Epoch: 0 -- Step: 30 -- d_loss: 0.02281767688691616 -- g_loss: 6.639409065246582
Epoch: 0 -- Step: 40 -- d_loss: 0.030390817672014236 -- g_loss: 8.16735553741455
Epoch: 0 -- Step: 50 -- d_loss: 4.331295967102051 -- g_loss: 10.53857135772705
Epoch: 0 -- Step: 60 -- d_loss: 0.7680845260620117 -- g_loss: 1.0419775247573853
Epoch: 0 -- Step: 70 -- d_loss: 0.2806212604045868 -- g_loss: 2.7464795112609863
Epoch: 0 -- Step: 80 -- d_loss: 0.594424843788147 -- g_loss: 1.356933832168579
Epoch: 0 -- Step: 90 -- d_loss: 0.7611536979675293 -- g_loss: 1.7591285705566406
Epoch: 0 -- Step: 100 -- d_loss: 1.1949225664138794 -- g_loss: 0.6835594177246094
Epoch: 0 -- Step: 110 -- d_loss: 1.532126784324646 -- g_loss: 0.47429269552230835
Epoch: 0 -- Step: 120 -- d_loss: 0.9302685260772705 -- g_loss: 1.0733888149261475
Epoch: 0 -- Step: 130 -- d_loss: 1.155641794204712 -- g_loss: 0.9005465507507324
Epoch: 0 -- Step: 140 -- d_loss: 1.834296703338623 -- g_loss: 0.2523607909679413
Epoch: 0 -- Step: 150 -- d_loss: 1.1264755725860596 -- g_loss: 0.6817317008972168
Epoch: 0 -- Step: 160 -- d_loss: 1.051430344581604 -- g_loss: 0.8035173416137695
Epoch: 0 -- Step: 170 -- d_loss: 1.0306580066680908 -- g_loss: 0.7705690860748291
Epoch: 0 -- Step: 180 -- d_loss: 1.166183590888977 -- g_loss: 1.5168412923812866
Epoch: 0 -- Step: 190 -- d_loss: 1.1633694171905518 -- g_loss: 0.9853212833404541
Epoch: 0 -- Step: 200 -- d_loss: 1.1337440013885498 -- g_loss: 0.6978400945663452
Epoch: 0 -- Step: 210 -- d_loss: 1.0198578834533691 -- g_loss: 1.161963701248169
Epoch: 0 -- Step: 220 -- d_loss: 1.0460155010223389 -- g_loss: 0.9255943894386292
Epoch: 0 -- Step: 230 -- d_loss: 1.0392814874649048 -- g_loss: 1.3703691959381104
Epoch: 0 -- Step: 240 -- d_loss: 1.197930932044983 -- g_loss: 0.5464293956756592
Epoch: 0 -- Step: 250 -- d_loss: 0.9769629240036011 -- g_loss: 1.4929358959197998
Epoch: 0 -- Step: 260 -- d_loss: 0.9208825826644897 -- g_loss: 1.2941476106643677
Epoch: 0 -- Step: 270 -- d_loss: 0.9873843193054199 -- g_loss: 1.5691750049591064
Epoch: 0 -- Step: 280 -- d_loss: 0.9561924934387207 -- g_loss: 1.158470869064331
Epoch: 0 -- Step: 290 -- d_loss: 1.0848631858825684 -- g_loss: 1.8248838186264038
Epoch: 0 -- Step: 300 -- d_loss: 0.9550783634185791 -- g_loss: 1.5365937948226929
Epoch: 0 -- Step: 310 -- d_loss: 0.9725456833839417 -- g_loss: 0.7777073383331299
Epoch: 0 -- Step: 320 -- d_loss: 1.6753404140472412 -- g_loss: 2.4892797470092773
Epoch: 0 -- Step: 330 -- d_loss: 1.0453178882598877 -- g_loss: 1.0394611358642578
Epoch: 0 -- Step: 340 -- d_loss: 1.359913945198059 -- g_loss: 0.39264750480651855
Epoch: 0 -- Step: 350 -- d_loss: 0.9763960242271423 -- g_loss: 0.9105708599090576
Epoch: 0 -- Step: 360 -- d_loss: 1.1472727060317993 -- g_loss: 0.5380191802978516
Epoch: 0 -- Step: 370 -- d_loss: 0.983116090297699 -- g_loss: 1.0143802165985107
Epoch: 0 -- Step: 380 -- d_loss: 1.1879642009735107 -- g_loss: 0.5547511577606201
Epoch: 0 -- Step: 390 -- d_loss: 0.9911061525344849 -- g_loss: 1.1106277704238892
Epoch: 0 -- Step: 400 -- d_loss: 1.033574104309082 -- g_loss: 1.387387990951538
Epoch: 0 -- Step: 410 -- d_loss: 0.9704635143280029 -- g_loss: 0.8986825942993164
Epoch: 0 -- Step: 420 -- d_loss: 1.0224109888076782 -- g_loss: 1.1413966417312622
Epoch: 0 -- Step: 430 -- d_loss: 0.94695645570755 -- g_loss: 1.0982029438018799
Epoch: 0 -- Step: 440 -- d_loss: 0.890794575214386 -- g_loss: 1.3059253692626953
Epoch: 0 -- Step: 450 -- d_loss: 1.0167725086212158 -- g_loss: 0.8158342838287354
Epoch: 0 -- Step: 460 -- d_loss: 0.9179112911224365 -- g_loss: 1.2322192192077637
Epoch: 1 -- Step: 470 -- d_loss: 1.1071112155914307 -- g_loss: 0.5699755549430847
Epoch: 1 -- Step: 480 -- d_loss: 1.009651780128479 -- g_loss: 1.1748476028442383
Epoch: 1 -- Step: 490 -- d_loss: 0.98896324634552 -- g_loss: 0.7256466150283813
Epoch: 1 -- Step: 500 -- d_loss: 1.0049916505813599 -- g_loss: 0.7055234909057617
Epoch: 1 -- Step: 510 -- d_loss: 0.9607968926429749 -- g_loss: 1.5129117965698242
Epoch: 1 -- Step: 520 -- d_loss: 0.9008792638778687 -- g_loss: 1.2569299936294556
Epoch: 1 -- Step: 530 -- d_loss: 1.0684170722961426 -- g_loss: 1.382836103439331
Epoch: 1 -- Step: 540 -- d_loss: 0.9576581716537476 -- g_loss: 1.3073469400405884
Epoch: 1 -- Step: 550 -- d_loss: 0.9453575611114502 -- g_loss: 1.049583077430725
Epoch: 1 -- Step: 560 -- d_loss: 1.0407055616378784 -- g_loss: 0.7289315462112427
Epoch: 1 -- Step: 570 -- d_loss: 0.9388839602470398 -- g_loss: 1.0104011297225952
Epoch: 1 -- Step: 580 -- d_loss: 1.0753720998764038 -- g_loss: 1.5687161684036255
Epoch: 1 -- Step: 590 -- d_loss: 0.9824951887130737 -- g_loss: 1.000942349433899
Epoch: 1 -- Step: 600 -- d_loss: 1.0155689716339111 -- g_loss: 0.8422719836235046
Epoch: 1 -- Step: 610 -- d_loss: 1.1190448999404907 -- g_loss: 1.241908311843872
Epoch: 1 -- Step: 620 -- d_loss: 1.0187878608703613 -- g_loss: 1.0736923217773438
Epoch: 1 -- Step: 630 -- d_loss: 1.017331600189209 -- g_loss: 0.7904312610626221
Epoch: 1 -- Step: 640 -- d_loss: 1.2177494764328003 -- g_loss: 0.5454082489013672
Epoch: 1 -- Step: 650 -- d_loss: 1.0041112899780273 -- g_loss: 0.7869515419006348
Epoch: 1 -- Step: 660 -- d_loss: 1.0074472427368164 -- g_loss: 0.7443562150001526
Epoch: 1 -- Step: 670 -- d_loss: 0.9385982751846313 -- g_loss: 1.08158278465271
Epoch: 1 -- Step: 680 -- d_loss: 0.9918556809425354 -- g_loss: 1.1653330326080322
Epoch: 1 -- Step: 690 -- d_loss: 0.9793219566345215 -- g_loss: 0.7843822836875916
Epoch: 1 -- Step: 700 -- d_loss: 1.4795786142349243 -- g_loss: 0.34337317943573
Epoch: 1 -- Step: 710 -- d_loss: 0.93310546875 -- g_loss: 1.6393282413482666
Epoch: 1 -- Step: 720 -- d_loss: 0.8216481804847717 -- g_loss: 1.1438655853271484
Epoch: 1 -- Step: 730 -- d_loss: 0.8867435455322266 -- g_loss: 1.4032829999923706
Epoch: 1 -- Step: 740 -- d_loss: 0.8842298984527588 -- g_loss: 1.3297260999679565
Epoch: 1 -- Step: 750 -- d_loss: 0.9585742354393005 -- g_loss: 0.7389783263206482
Epoch: 1 -- Step: 760 -- d_loss: 0.8262853622436523 -- g_loss: 1.0515427589416504
Epoch: 1 -- Step: 770 -- d_loss: 1.0015227794647217 -- g_loss: 1.4153945446014404
Epoch: 1 -- Step: 780 -- d_loss: 0.9721543788909912 -- g_loss: 0.8989022970199585
Epoch: 1 -- Step: 790 -- d_loss: 0.875064492225647 -- g_loss: 0.9502137899398804
Epoch: 1 -- Step: 800 -- d_loss: 0.8562411665916443 -- g_loss: 0.9120610356330872
Epoch: 1 -- Step: 810 -- d_loss: 0.9958223700523376 -- g_loss: 0.6282020211219788
Epoch: 1 -- Step: 820 -- d_loss: 0.9233318567276001 -- g_loss: 0.8647503852844238
Epoch: 1 -- Step: 830 -- d_loss: 1.004600167274475 -- g_loss: 2.060114622116089
Epoch: 1 -- Step: 840 -- d_loss: 0.9357266426086426 -- g_loss: 0.8173389434814453
Epoch: 1 -- Step: 850 -- d_loss: 0.8042709231376648 -- g_loss: 1.0028992891311646
Epoch: 1 -- Step: 860 -- d_loss: 0.9073479771614075 -- g_loss: 0.9639356136322021
Epoch: 1 -- Step: 870 -- d_loss: 0.8810655474662781 -- g_loss: 0.8622294664382935
Epoch: 1 -- Step: 880 -- d_loss: 0.8318590521812439 -- g_loss: 1.4579449892044067
Epoch: 1 -- Step: 890 -- d_loss: 0.9475924968719482 -- g_loss: 0.7071715593338013
Epoch: 1 -- Step: 900 -- d_loss: 1.0195789337158203 -- g_loss: 0.6908923983573914
Epoch: 1 -- Step: 910 -- d_loss: 0.8795006275177002 -- g_loss: 0.8145804405212402
Epoch: 1 -- Step: 920 -- d_loss: 1.038091778755188 -- g_loss: 0.6284948587417603
Epoch: 1 -- Step: 930 -- d_loss: 0.7910155653953552 -- g_loss: 1.2550705671310425

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [20]:
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 20

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch: 0 -- Step: 10 -- d_loss: 0.6189572811126709 -- g_loss: 1.6682405471801758
Epoch: 0 -- Step: 20 -- d_loss: 0.8532211780548096 -- g_loss: 2.508634567260742
Epoch: 0 -- Step: 30 -- d_loss: 0.9677637219429016 -- g_loss: 2.884580135345459
Epoch: 0 -- Step: 40 -- d_loss: 1.280186414718628 -- g_loss: 0.5198928117752075
Epoch: 0 -- Step: 50 -- d_loss: 1.1457328796386719 -- g_loss: 0.5005475878715515
Epoch: 0 -- Step: 60 -- d_loss: 0.6648433804512024 -- g_loss: 2.4754750728607178
Epoch: 0 -- Step: 70 -- d_loss: 0.4169224202632904 -- g_loss: 1.8632996082305908
Epoch: 0 -- Step: 80 -- d_loss: 0.9443435072898865 -- g_loss: 0.7916808724403381
Epoch: 0 -- Step: 90 -- d_loss: 0.47528398036956787 -- g_loss: 1.4359049797058105
Epoch: 0 -- Step: 100 -- d_loss: 1.1192724704742432 -- g_loss: 0.7107819318771362
Epoch: 0 -- Step: 110 -- d_loss: 0.9428191781044006 -- g_loss: 0.7804093360900879
Epoch: 0 -- Step: 120 -- d_loss: 0.28318312764167786 -- g_loss: 2.026604175567627
Epoch: 0 -- Step: 130 -- d_loss: 0.3575264513492584 -- g_loss: 2.76959228515625
Epoch: 0 -- Step: 140 -- d_loss: 0.8777046203613281 -- g_loss: 0.7794727087020874
Epoch: 0 -- Step: 150 -- d_loss: 0.22578366100788116 -- g_loss: 3.62062406539917
Epoch: 0 -- Step: 160 -- d_loss: 0.13888703286647797 -- g_loss: 4.594459056854248
Epoch: 0 -- Step: 170 -- d_loss: 0.3556753396987915 -- g_loss: 1.8537588119506836
Epoch: 0 -- Step: 180 -- d_loss: 0.21600377559661865 -- g_loss: 2.78483247756958
Epoch: 0 -- Step: 190 -- d_loss: 0.7178511619567871 -- g_loss: 1.0621424913406372
Epoch: 0 -- Step: 200 -- d_loss: 1.7409213781356812 -- g_loss: 0.3326241374015808
Epoch: 0 -- Step: 210 -- d_loss: 1.1162914037704468 -- g_loss: 0.6142660975456238
Epoch: 0 -- Step: 220 -- d_loss: 1.187780737876892 -- g_loss: 0.5684333443641663
Epoch: 0 -- Step: 230 -- d_loss: 0.31298547983169556 -- g_loss: 2.8665857315063477
Epoch: 0 -- Step: 240 -- d_loss: 0.3061736226081848 -- g_loss: 4.465298652648926
Epoch: 0 -- Step: 250 -- d_loss: 0.5903671979904175 -- g_loss: 1.8480724096298218
Epoch: 0 -- Step: 260 -- d_loss: 0.46069276332855225 -- g_loss: 2.3626768589019775
Epoch: 0 -- Step: 270 -- d_loss: 1.2914705276489258 -- g_loss: 1.207122802734375
Epoch: 0 -- Step: 280 -- d_loss: 0.40697237849235535 -- g_loss: 2.079935312271118
Epoch: 0 -- Step: 290 -- d_loss: 0.7015174627304077 -- g_loss: 0.9863153696060181
Epoch: 0 -- Step: 300 -- d_loss: 3.0196330547332764 -- g_loss: 4.714893341064453
Epoch: 0 -- Step: 310 -- d_loss: 0.684442937374115 -- g_loss: 1.6667400598526
Epoch: 0 -- Step: 320 -- d_loss: 1.2834985256195068 -- g_loss: 0.3918673098087311
Epoch: 0 -- Step: 330 -- d_loss: 1.095014214515686 -- g_loss: 0.5890215635299683
Epoch: 0 -- Step: 340 -- d_loss: 1.3010108470916748 -- g_loss: 3.9024429321289062
Epoch: 0 -- Step: 350 -- d_loss: 1.0750625133514404 -- g_loss: 1.6504697799682617
Epoch: 0 -- Step: 360 -- d_loss: 0.8633740544319153 -- g_loss: 1.1450884342193604
Epoch: 0 -- Step: 370 -- d_loss: 0.655682384967804 -- g_loss: 1.1551790237426758
Epoch: 0 -- Step: 380 -- d_loss: 0.32378149032592773 -- g_loss: 1.840850830078125
Epoch: 0 -- Step: 390 -- d_loss: 0.5401840209960938 -- g_loss: 2.5258617401123047
Epoch: 0 -- Step: 400 -- d_loss: 1.4231135845184326 -- g_loss: 1.0094537734985352
Epoch: 0 -- Step: 410 -- d_loss: 1.5186538696289062 -- g_loss: 0.3039957284927368
Epoch: 0 -- Step: 420 -- d_loss: 0.8933823704719543 -- g_loss: 1.017946481704712
Epoch: 0 -- Step: 430 -- d_loss: 0.5030015707015991 -- g_loss: 1.7307782173156738
Epoch: 0 -- Step: 440 -- d_loss: 0.6974966526031494 -- g_loss: 1.0915714502334595
Epoch: 0 -- Step: 450 -- d_loss: 1.3992705345153809 -- g_loss: 2.4568943977355957
Epoch: 0 -- Step: 460 -- d_loss: 1.432362675666809 -- g_loss: 0.3547859191894531
Epoch: 0 -- Step: 470 -- d_loss: 0.3885296881198883 -- g_loss: 3.3084805011749268
Epoch: 0 -- Step: 480 -- d_loss: 1.4424132108688354 -- g_loss: 3.7159488201141357
Epoch: 0 -- Step: 490 -- d_loss: 0.6583214998245239 -- g_loss: 1.8128461837768555
Epoch: 0 -- Step: 500 -- d_loss: 1.0541586875915527 -- g_loss: 1.4473506212234497
Epoch: 0 -- Step: 510 -- d_loss: 0.7510514259338379 -- g_loss: 2.025989294052124
Epoch: 0 -- Step: 520 -- d_loss: 0.4676828980445862 -- g_loss: 1.976776123046875
Epoch: 0 -- Step: 530 -- d_loss: 0.7727198600769043 -- g_loss: 1.4416558742523193
Epoch: 0 -- Step: 540 -- d_loss: 1.3622641563415527 -- g_loss: 1.1495091915130615
Epoch: 0 -- Step: 550 -- d_loss: 0.8533591032028198 -- g_loss: 1.0452699661254883
Epoch: 0 -- Step: 560 -- d_loss: 0.4185512661933899 -- g_loss: 2.6592907905578613
Epoch: 0 -- Step: 570 -- d_loss: 0.41667357087135315 -- g_loss: 1.8484811782836914
Epoch: 0 -- Step: 580 -- d_loss: 1.1975208520889282 -- g_loss: 1.084951400756836
Epoch: 0 -- Step: 590 -- d_loss: 0.40823841094970703 -- g_loss: 1.647853970527649
Epoch: 0 -- Step: 600 -- d_loss: 1.4130827188491821 -- g_loss: 0.6910707354545593
Epoch: 0 -- Step: 610 -- d_loss: 0.39678677916526794 -- g_loss: 2.560805320739746
Epoch: 0 -- Step: 620 -- d_loss: 0.6609561443328857 -- g_loss: 1.0886285305023193
Epoch: 0 -- Step: 630 -- d_loss: 1.5914608240127563 -- g_loss: 3.239980936050415
Epoch: 0 -- Step: 640 -- d_loss: 0.9681727886199951 -- g_loss: 1.6076397895812988
Epoch: 0 -- Step: 650 -- d_loss: 0.7274972200393677 -- g_loss: 2.520704507827759
Epoch: 0 -- Step: 660 -- d_loss: 0.8910841941833496 -- g_loss: 0.8940232992172241
Epoch: 0 -- Step: 670 -- d_loss: 0.4553184509277344 -- g_loss: 1.965212106704712
Epoch: 0 -- Step: 680 -- d_loss: 0.6510272026062012 -- g_loss: 1.2685779333114624
Epoch: 0 -- Step: 690 -- d_loss: 0.8435059785842896 -- g_loss: 2.0915842056274414
Epoch: 0 -- Step: 700 -- d_loss: 0.8578300476074219 -- g_loss: 0.823190450668335
Epoch: 0 -- Step: 710 -- d_loss: 0.4227743446826935 -- g_loss: 2.2841506004333496
Epoch: 0 -- Step: 720 -- d_loss: 0.39046141505241394 -- g_loss: 3.2438244819641113
Epoch: 0 -- Step: 730 -- d_loss: 1.5941988229751587 -- g_loss: 4.497476577758789
Epoch: 0 -- Step: 740 -- d_loss: 0.7174882888793945 -- g_loss: 1.9809353351593018
Epoch: 0 -- Step: 750 -- d_loss: 0.40130746364593506 -- g_loss: 2.518244743347168
Epoch: 0 -- Step: 760 -- d_loss: 0.633373498916626 -- g_loss: 3.046576976776123
Epoch: 0 -- Step: 770 -- d_loss: 0.9137560129165649 -- g_loss: 1.9444615840911865
Epoch: 0 -- Step: 780 -- d_loss: 0.6419381499290466 -- g_loss: 2.358516216278076
Epoch: 0 -- Step: 790 -- d_loss: 1.4742600917816162 -- g_loss: 0.594638466835022
Epoch: 0 -- Step: 800 -- d_loss: 0.9166625142097473 -- g_loss: 2.0175764560699463
Epoch: 0 -- Step: 810 -- d_loss: 0.7758190035820007 -- g_loss: 0.730849027633667
Epoch: 0 -- Step: 820 -- d_loss: 1.4578440189361572 -- g_loss: 0.3882875442504883
Epoch: 0 -- Step: 830 -- d_loss: 0.44379448890686035 -- g_loss: 2.494309186935425
Epoch: 0 -- Step: 840 -- d_loss: 0.3009606599807739 -- g_loss: 3.6628260612487793
Epoch: 0 -- Step: 850 -- d_loss: 0.5243213772773743 -- g_loss: 1.4126404523849487
Epoch: 0 -- Step: 860 -- d_loss: 1.6131051778793335 -- g_loss: 1.1347616910934448
Epoch: 0 -- Step: 870 -- d_loss: 1.640211582183838 -- g_loss: 0.30656397342681885
Epoch: 0 -- Step: 880 -- d_loss: 1.8730353116989136 -- g_loss: 0.24091392755508423
Epoch: 0 -- Step: 890 -- d_loss: 0.7271528840065002 -- g_loss: 1.2307286262512207
Epoch: 0 -- Step: 900 -- d_loss: 0.45269912481307983 -- g_loss: 2.506349563598633
Epoch: 0 -- Step: 910 -- d_loss: 1.7205414772033691 -- g_loss: 0.3101435899734497
Epoch: 0 -- Step: 920 -- d_loss: 0.778600811958313 -- g_loss: 1.340665340423584
Epoch: 0 -- Step: 930 -- d_loss: 0.416753888130188 -- g_loss: 3.8478164672851562
Epoch: 0 -- Step: 940 -- d_loss: 0.6839836835861206 -- g_loss: 1.671226143836975
Epoch: 0 -- Step: 950 -- d_loss: 0.21385543048381805 -- g_loss: 3.712894916534424
Epoch: 0 -- Step: 960 -- d_loss: 1.444953441619873 -- g_loss: 0.5507619380950928
Epoch: 0 -- Step: 970 -- d_loss: 1.3159905672073364 -- g_loss: 0.5107996463775635
Epoch: 0 -- Step: 980 -- d_loss: 1.3881958723068237 -- g_loss: 0.5218123197555542
Epoch: 0 -- Step: 990 -- d_loss: 1.3753917217254639 -- g_loss: 0.6706399917602539
Epoch: 0 -- Step: 1000 -- d_loss: 1.1528619527816772 -- g_loss: 0.8228816986083984
Epoch: 0 -- Step: 1010 -- d_loss: 0.9613427519798279 -- g_loss: 1.3325039148330688
Epoch: 0 -- Step: 1020 -- d_loss: 0.6985876560211182 -- g_loss: 2.3830606937408447
Epoch: 0 -- Step: 1030 -- d_loss: 1.4630494117736816 -- g_loss: 0.80255126953125
Epoch: 0 -- Step: 1040 -- d_loss: 1.2302714586257935 -- g_loss: 0.933463454246521
Epoch: 0 -- Step: 1050 -- d_loss: 0.9717111587524414 -- g_loss: 1.260368824005127
Epoch: 0 -- Step: 1060 -- d_loss: 0.5876362323760986 -- g_loss: 1.2069242000579834
Epoch: 0 -- Step: 1070 -- d_loss: 1.1813292503356934 -- g_loss: 1.2407647371292114
Epoch: 0 -- Step: 1080 -- d_loss: 0.3050784468650818 -- g_loss: 2.800346851348877
Epoch: 0 -- Step: 1090 -- d_loss: 2.4665019512176514 -- g_loss: 3.7656726837158203
Epoch: 0 -- Step: 1100 -- d_loss: 1.0486547946929932 -- g_loss: 1.5476114749908447
Epoch: 0 -- Step: 1110 -- d_loss: 0.9820250272750854 -- g_loss: 0.8171892166137695
Epoch: 0 -- Step: 1120 -- d_loss: 1.6059978008270264 -- g_loss: 0.3154286742210388
Epoch: 0 -- Step: 1130 -- d_loss: 0.9904441833496094 -- g_loss: 1.22186279296875
Epoch: 0 -- Step: 1140 -- d_loss: 0.2859627604484558 -- g_loss: 2.696101188659668
Epoch: 0 -- Step: 1150 -- d_loss: 1.6777154207229614 -- g_loss: 4.121460437774658
Epoch: 0 -- Step: 1160 -- d_loss: 0.6745917797088623 -- g_loss: 0.8780162334442139
Epoch: 0 -- Step: 1170 -- d_loss: 0.9326121807098389 -- g_loss: 1.2943236827850342
Epoch: 0 -- Step: 1180 -- d_loss: 0.5793116688728333 -- g_loss: 1.0676884651184082
Epoch: 0 -- Step: 1190 -- d_loss: 1.2927860021591187 -- g_loss: 0.6350408792495728
Epoch: 0 -- Step: 1200 -- d_loss: 1.3902781009674072 -- g_loss: 0.6302004456520081
Epoch: 0 -- Step: 1210 -- d_loss: 1.1440271139144897 -- g_loss: 0.7656934261322021
Epoch: 0 -- Step: 1220 -- d_loss: 1.3002328872680664 -- g_loss: 0.715445876121521
Epoch: 0 -- Step: 1230 -- d_loss: 1.2937426567077637 -- g_loss: 0.6865297555923462
Epoch: 0 -- Step: 1240 -- d_loss: 1.5434268712997437 -- g_loss: 0.6973769664764404
Epoch: 0 -- Step: 1250 -- d_loss: 1.4455633163452148 -- g_loss: 0.5808602571487427
Epoch: 0 -- Step: 1260 -- d_loss: 1.0961345434188843 -- g_loss: 0.8604573607444763
Epoch: 0 -- Step: 1270 -- d_loss: 0.9594032168388367 -- g_loss: 1.9071661233901978
Epoch: 0 -- Step: 1280 -- d_loss: 0.9304260015487671 -- g_loss: 1.3109021186828613
Epoch: 0 -- Step: 1290 -- d_loss: 1.1514148712158203 -- g_loss: 0.7132852673530579
Epoch: 0 -- Step: 1300 -- d_loss: 1.1793675422668457 -- g_loss: 1.0359140634536743
Epoch: 0 -- Step: 1310 -- d_loss: 1.0821325778961182 -- g_loss: 1.1243438720703125
Epoch: 0 -- Step: 1320 -- d_loss: 1.0742340087890625 -- g_loss: 1.3470410108566284
Epoch: 0 -- Step: 1330 -- d_loss: 0.42404162883758545 -- g_loss: 1.7010838985443115
Epoch: 0 -- Step: 1340 -- d_loss: 1.1779853105545044 -- g_loss: 2.0520894527435303
Epoch: 0 -- Step: 1350 -- d_loss: 1.0360634326934814 -- g_loss: 0.9936537742614746
Epoch: 0 -- Step: 1360 -- d_loss: 0.9809082746505737 -- g_loss: 0.6320120096206665
Epoch: 0 -- Step: 1370 -- d_loss: 0.8686448335647583 -- g_loss: 1.4107683897018433
Epoch: 0 -- Step: 1380 -- d_loss: 1.162491798400879 -- g_loss: 0.906292200088501
Epoch: 0 -- Step: 1390 -- d_loss: 1.1113702058792114 -- g_loss: 0.891510009765625
Epoch: 0 -- Step: 1400 -- d_loss: 0.2266317754983902 -- g_loss: 3.0489165782928467
Epoch: 0 -- Step: 1410 -- d_loss: 1.035365104675293 -- g_loss: 0.6253972053527832
Epoch: 0 -- Step: 1420 -- d_loss: 0.6521323323249817 -- g_loss: 1.610490322113037
Epoch: 0 -- Step: 1430 -- d_loss: 0.6945587396621704 -- g_loss: 1.284684181213379
Epoch: 0 -- Step: 1440 -- d_loss: 0.39973515272140503 -- g_loss: 2.2424755096435547
Epoch: 0 -- Step: 1450 -- d_loss: 0.9458611011505127 -- g_loss: 0.6484680771827698
Epoch: 0 -- Step: 1460 -- d_loss: 0.22823208570480347 -- g_loss: 2.924443006515503
Epoch: 0 -- Step: 1470 -- d_loss: 0.6245657801628113 -- g_loss: 0.9658270478248596
Epoch: 0 -- Step: 1480 -- d_loss: 0.19764626026153564 -- g_loss: 2.3248441219329834
Epoch: 0 -- Step: 1490 -- d_loss: 1.6478025913238525 -- g_loss: 0.2690458297729492
Epoch: 0 -- Step: 1500 -- d_loss: 0.8623889684677124 -- g_loss: 2.9659221172332764
Epoch: 0 -- Step: 1510 -- d_loss: 1.5176411867141724 -- g_loss: 1.7084048986434937
Epoch: 0 -- Step: 1520 -- d_loss: 1.014229655265808 -- g_loss: 1.354475975036621
Epoch: 0 -- Step: 1530 -- d_loss: 0.15211786329746246 -- g_loss: 4.3755598068237305
Epoch: 0 -- Step: 1540 -- d_loss: 1.0428096055984497 -- g_loss: 0.5147484540939331
Epoch: 0 -- Step: 1550 -- d_loss: 0.36802083253860474 -- g_loss: 1.5795575380325317
Epoch: 0 -- Step: 1560 -- d_loss: 0.46364694833755493 -- g_loss: 1.7814652919769287
Epoch: 0 -- Step: 1570 -- d_loss: 0.921968936920166 -- g_loss: 0.8456337451934814
Epoch: 0 -- Step: 1580 -- d_loss: 1.2094566822052002 -- g_loss: 0.721060574054718
Epoch: 1 -- Step: 1590 -- d_loss: 1.2473468780517578 -- g_loss: 0.7497451305389404
Epoch: 1 -- Step: 1600 -- d_loss: 0.6745474338531494 -- g_loss: 2.7854301929473877
Epoch: 1 -- Step: 1610 -- d_loss: 0.9143364429473877 -- g_loss: 1.1025044918060303
Epoch: 1 -- Step: 1620 -- d_loss: 1.008831262588501 -- g_loss: 0.5783803462982178
Epoch: 1 -- Step: 1630 -- d_loss: 1.249293565750122 -- g_loss: 0.530289888381958
Epoch: 1 -- Step: 1640 -- d_loss: 0.7459416389465332 -- g_loss: 1.271433711051941
Epoch: 1 -- Step: 1650 -- d_loss: 1.446341633796692 -- g_loss: 0.4242762625217438
Epoch: 1 -- Step: 1660 -- d_loss: 0.9493142366409302 -- g_loss: 1.037452220916748
Epoch: 1 -- Step: 1670 -- d_loss: 0.7378976941108704 -- g_loss: 1.6037817001342773
Epoch: 1 -- Step: 1680 -- d_loss: 0.2288271188735962 -- g_loss: 2.5756006240844727
Epoch: 1 -- Step: 1690 -- d_loss: 0.8916651606559753 -- g_loss: 0.7521088123321533
Epoch: 1 -- Step: 1700 -- d_loss: 0.4584102928638458 -- g_loss: 1.3949971199035645
Epoch: 1 -- Step: 1710 -- d_loss: 1.0619703531265259 -- g_loss: 0.5304038524627686
Epoch: 1 -- Step: 1720 -- d_loss: 0.897750735282898 -- g_loss: 2.481067180633545
Epoch: 1 -- Step: 1730 -- d_loss: 0.7138628959655762 -- g_loss: 1.9476619958877563
Epoch: 1 -- Step: 1740 -- d_loss: 0.6286993026733398 -- g_loss: 1.0541020631790161
Epoch: 1 -- Step: 1750 -- d_loss: 0.32747936248779297 -- g_loss: 2.8508076667785645
Epoch: 1 -- Step: 1760 -- d_loss: 1.001863718032837 -- g_loss: 1.3201563358306885
Epoch: 1 -- Step: 1770 -- d_loss: 0.36181312799453735 -- g_loss: 1.5450842380523682
Epoch: 1 -- Step: 1780 -- d_loss: 1.144686222076416 -- g_loss: 3.2428181171417236
Epoch: 1 -- Step: 1790 -- d_loss: 1.490280032157898 -- g_loss: 0.8920574188232422
Epoch: 1 -- Step: 1800 -- d_loss: 0.9011308550834656 -- g_loss: 1.3165229558944702
Epoch: 1 -- Step: 1810 -- d_loss: 0.8414962887763977 -- g_loss: 0.9211695194244385
Epoch: 1 -- Step: 1820 -- d_loss: 1.1024104356765747 -- g_loss: 0.5241304636001587
Epoch: 1 -- Step: 1830 -- d_loss: 0.17971862852573395 -- g_loss: 3.7634243965148926
Epoch: 1 -- Step: 1840 -- d_loss: 0.6849057674407959 -- g_loss: 0.9918780326843262
Epoch: 1 -- Step: 1850 -- d_loss: 0.7813336849212646 -- g_loss: 2.1332669258117676
Epoch: 1 -- Step: 1860 -- d_loss: 0.08805727958679199 -- g_loss: 4.440838813781738
Epoch: 1 -- Step: 1870 -- d_loss: 0.38721761107444763 -- g_loss: 2.508232593536377
Epoch: 1 -- Step: 1880 -- d_loss: 1.4452531337738037 -- g_loss: 0.4298607409000397
Epoch: 1 -- Step: 1890 -- d_loss: 1.0737653970718384 -- g_loss: 0.797720193862915
Epoch: 1 -- Step: 1900 -- d_loss: 1.0560133457183838 -- g_loss: 1.833406925201416
Epoch: 1 -- Step: 1910 -- d_loss: 0.6736504435539246 -- g_loss: 1.4597465991973877
Epoch: 1 -- Step: 1920 -- d_loss: 0.3199922442436218 -- g_loss: 4.544075012207031
Epoch: 1 -- Step: 1930 -- d_loss: 0.4952291250228882 -- g_loss: 3.423349142074585
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Epoch: 1 -- Step: 1950 -- d_loss: 0.6771110892295837 -- g_loss: 4.860257148742676
Epoch: 1 -- Step: 1960 -- d_loss: 1.0452930927276611 -- g_loss: 0.7398726344108582
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Epoch: 1 -- Step: 1990 -- d_loss: 0.44811874628067017 -- g_loss: 2.763991355895996
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Epoch: 2 -- Step: 3950 -- d_loss: 0.6537075638771057 -- g_loss: 2.4307703971862793
Epoch: 2 -- Step: 3960 -- d_loss: 1.092867136001587 -- g_loss: 0.5515989661216736
Epoch: 2 -- Step: 3970 -- d_loss: 1.0143593549728394 -- g_loss: 0.9416922926902771
Epoch: 2 -- Step: 3980 -- d_loss: 0.5799768567085266 -- g_loss: 1.2054327726364136
Epoch: 2 -- Step: 3990 -- d_loss: 1.065787672996521 -- g_loss: 0.7175549864768982
Epoch: 2 -- Step: 4000 -- d_loss: 1.0736268758773804 -- g_loss: 0.9380308985710144
Epoch: 2 -- Step: 4010 -- d_loss: 0.49524644017219543 -- g_loss: 1.6278610229492188
Epoch: 2 -- Step: 4020 -- d_loss: 0.5700353980064392 -- g_loss: 3.399916410446167
Epoch: 2 -- Step: 4030 -- d_loss: 0.29479190707206726 -- g_loss: 2.682537078857422
Epoch: 2 -- Step: 4040 -- d_loss: 1.5328028202056885 -- g_loss: 0.3403250277042389
Epoch: 2 -- Step: 4050 -- d_loss: 1.1912624835968018 -- g_loss: 0.9502177834510803
Epoch: 2 -- Step: 4060 -- d_loss: 1.4540774822235107 -- g_loss: 0.37589454650878906
Epoch: 2 -- Step: 4070 -- d_loss: 0.7142375707626343 -- g_loss: 2.816758632659912
Epoch: 2 -- Step: 4080 -- d_loss: 0.6742832660675049 -- g_loss: 1.7923628091812134
Epoch: 2 -- Step: 4090 -- d_loss: 1.131827473640442 -- g_loss: 0.5962620377540588
Epoch: 2 -- Step: 4100 -- d_loss: 0.7776491641998291 -- g_loss: 1.5221519470214844
Epoch: 2 -- Step: 4110 -- d_loss: 1.296432375907898 -- g_loss: 1.7925742864608765
Epoch: 2 -- Step: 4120 -- d_loss: 0.9120518565177917 -- g_loss: 1.806172490119934
Epoch: 2 -- Step: 4130 -- d_loss: 0.8554316163063049 -- g_loss: 1.6359946727752686
Epoch: 2 -- Step: 4140 -- d_loss: 0.3466651439666748 -- g_loss: 2.4593915939331055
Epoch: 2 -- Step: 4150 -- d_loss: 1.902287483215332 -- g_loss: 2.714090347290039
Epoch: 2 -- Step: 4160 -- d_loss: 0.08307395130395889 -- g_loss: 7.267695426940918
Epoch: 2 -- Step: 4170 -- d_loss: 0.9030163884162903 -- g_loss: 0.7578636407852173
Epoch: 2 -- Step: 4180 -- d_loss: 1.286759853363037 -- g_loss: 0.42272430658340454
Epoch: 2 -- Step: 4190 -- d_loss: 1.203334093093872 -- g_loss: 0.5179607272148132
Epoch: 2 -- Step: 4200 -- d_loss: 0.9875295758247375 -- g_loss: 0.6481342315673828
Epoch: 2 -- Step: 4210 -- d_loss: 1.289412260055542 -- g_loss: 0.5204260349273682
Epoch: 2 -- Step: 4220 -- d_loss: 0.9167395830154419 -- g_loss: 1.954056739807129
Epoch: 2 -- Step: 4230 -- d_loss: 1.3024401664733887 -- g_loss: 0.46617376804351807
Epoch: 2 -- Step: 4240 -- d_loss: 0.8536108136177063 -- g_loss: 0.7725452780723572
Epoch: 2 -- Step: 4250 -- d_loss: 0.7554200887680054 -- g_loss: 1.42948317527771
Epoch: 2 -- Step: 4260 -- d_loss: 1.2968056201934814 -- g_loss: 0.9223647117614746
Epoch: 2 -- Step: 4270 -- d_loss: 1.5667248964309692 -- g_loss: 0.4492717683315277
Epoch: 2 -- Step: 4280 -- d_loss: 0.8849894404411316 -- g_loss: 1.1901586055755615
Epoch: 2 -- Step: 4290 -- d_loss: 1.1160900592803955 -- g_loss: 0.892753541469574
Epoch: 2 -- Step: 4300 -- d_loss: 0.602445125579834 -- g_loss: 1.9186862707138062
Epoch: 2 -- Step: 4310 -- d_loss: 1.1005351543426514 -- g_loss: 1.2975653409957886
Epoch: 2 -- Step: 4320 -- d_loss: 1.4000720977783203 -- g_loss: 0.791260838508606
Epoch: 2 -- Step: 4330 -- d_loss: 1.0205223560333252 -- g_loss: 0.6119193434715271
Epoch: 2 -- Step: 4340 -- d_loss: 1.0044894218444824 -- g_loss: 0.7549805641174316
Epoch: 2 -- Step: 4350 -- d_loss: 0.7490428686141968 -- g_loss: 1.8998996019363403
Epoch: 2 -- Step: 4360 -- d_loss: 0.9470696449279785 -- g_loss: 0.9477285742759705
Epoch: 2 -- Step: 4370 -- d_loss: 1.0221588611602783 -- g_loss: 0.7816911935806274
Epoch: 2 -- Step: 4380 -- d_loss: 0.7640642523765564 -- g_loss: 1.2674691677093506
Epoch: 2 -- Step: 4390 -- d_loss: 1.0103931427001953 -- g_loss: 1.1449129581451416
Epoch: 2 -- Step: 4400 -- d_loss: 0.8146729469299316 -- g_loss: 1.723823070526123
Epoch: 2 -- Step: 4410 -- d_loss: 0.797262966632843 -- g_loss: 1.0392987728118896
Epoch: 2 -- Step: 4420 -- d_loss: 0.956682026386261 -- g_loss: 0.8275312185287476
Epoch: 2 -- Step: 4430 -- d_loss: 0.6455158591270447 -- g_loss: 1.1909425258636475
Epoch: 2 -- Step: 4440 -- d_loss: 1.172816514968872 -- g_loss: 0.7637572884559631
Epoch: 2 -- Step: 4450 -- d_loss: 0.8283816576004028 -- g_loss: 1.0262383222579956
Epoch: 2 -- Step: 4460 -- d_loss: 0.9379554390907288 -- g_loss: 0.9616621732711792
Epoch: 2 -- Step: 4470 -- d_loss: 0.9448939561843872 -- g_loss: 1.5062098503112793
Epoch: 2 -- Step: 4480 -- d_loss: 1.158558964729309 -- g_loss: 2.4880781173706055
Epoch: 2 -- Step: 4490 -- d_loss: 1.210967779159546 -- g_loss: 0.5548526644706726
Epoch: 2 -- Step: 4500 -- d_loss: 0.26027634739875793 -- g_loss: 4.278383731842041
Epoch: 2 -- Step: 4510 -- d_loss: 0.5499380826950073 -- g_loss: 1.4243980646133423
Epoch: 2 -- Step: 4520 -- d_loss: 0.6024597883224487 -- g_loss: 5.156578540802002
Epoch: 2 -- Step: 4530 -- d_loss: 0.9747189283370972 -- g_loss: 0.7755573987960815
Epoch: 2 -- Step: 4540 -- d_loss: 0.6433472633361816 -- g_loss: 2.24735426902771
Epoch: 2 -- Step: 4550 -- d_loss: 1.3030067682266235 -- g_loss: 0.45421555638313293
Epoch: 2 -- Step: 4560 -- d_loss: 1.190370798110962 -- g_loss: 0.6806381344795227
Epoch: 2 -- Step: 4570 -- d_loss: 0.40899771451950073 -- g_loss: 1.9386446475982666
Epoch: 2 -- Step: 4580 -- d_loss: 1.3571817874908447 -- g_loss: 0.4291446805000305
Epoch: 2 -- Step: 4590 -- d_loss: 0.32681572437286377 -- g_loss: 2.1676254272460938
Epoch: 2 -- Step: 4600 -- d_loss: 0.9201475977897644 -- g_loss: 0.9527219533920288
Epoch: 2 -- Step: 4610 -- d_loss: 0.7532637119293213 -- g_loss: 1.1414250135421753
Epoch: 2 -- Step: 4620 -- d_loss: 1.0481843948364258 -- g_loss: 0.8851184844970703
Epoch: 2 -- Step: 4630 -- d_loss: 1.1702464818954468 -- g_loss: 0.7170353531837463
Epoch: 2 -- Step: 4640 -- d_loss: 1.0761473178863525 -- g_loss: 0.8203911781311035
Epoch: 2 -- Step: 4650 -- d_loss: 1.1074144840240479 -- g_loss: 0.5884042978286743
Epoch: 2 -- Step: 4660 -- d_loss: 1.0406407117843628 -- g_loss: 0.8615257740020752
Epoch: 2 -- Step: 4670 -- d_loss: 1.002254605293274 -- g_loss: 0.6978989839553833
Epoch: 2 -- Step: 4680 -- d_loss: 1.0568811893463135 -- g_loss: 0.8664877414703369
Epoch: 2 -- Step: 4690 -- d_loss: 0.8699911236763 -- g_loss: 1.4517979621887207
Epoch: 2 -- Step: 4700 -- d_loss: 0.9394158124923706 -- g_loss: 0.8424553871154785
Epoch: 2 -- Step: 4710 -- d_loss: 1.002976417541504 -- g_loss: 0.7617977261543274
Epoch: 2 -- Step: 4720 -- d_loss: 1.3814973831176758 -- g_loss: 0.4685993790626526
Epoch: 2 -- Step: 4730 -- d_loss: 0.7285329103469849 -- g_loss: 1.1691986322402954
Epoch: 2 -- Step: 4740 -- d_loss: 0.9048235416412354 -- g_loss: 0.7851202487945557
Epoch: 3 -- Step: 4750 -- d_loss: 0.8240299820899963 -- g_loss: 0.9800426363945007
Epoch: 3 -- Step: 4760 -- d_loss: 1.175516128540039 -- g_loss: 0.5262134075164795
Epoch: 3 -- Step: 4770 -- d_loss: 0.9847347736358643 -- g_loss: 1.231579303741455
Epoch: 3 -- Step: 4780 -- d_loss: 1.1977438926696777 -- g_loss: 0.7252588272094727
Epoch: 3 -- Step: 4790 -- d_loss: 1.034786343574524 -- g_loss: 0.6686787605285645
Epoch: 3 -- Step: 4800 -- d_loss: 1.0495612621307373 -- g_loss: 1.0435450077056885
Epoch: 3 -- Step: 4810 -- d_loss: 1.0089670419692993 -- g_loss: 0.7771851420402527
Epoch: 3 -- Step: 4820 -- d_loss: 0.7196758985519409 -- g_loss: 1.9954019784927368
Epoch: 3 -- Step: 4830 -- d_loss: 1.3503437042236328 -- g_loss: 2.3564045429229736
Epoch: 3 -- Step: 4840 -- d_loss: 0.8164265751838684 -- g_loss: 0.7325164079666138
Epoch: 3 -- Step: 4850 -- d_loss: 0.9229680299758911 -- g_loss: 0.8851931095123291
Epoch: 3 -- Step: 4860 -- d_loss: 0.4089655876159668 -- g_loss: 2.7609596252441406
Epoch: 3 -- Step: 4870 -- d_loss: 1.101258635520935 -- g_loss: 0.5967549085617065
Epoch: 3 -- Step: 4880 -- d_loss: 1.252939224243164 -- g_loss: 0.5346750020980835
Epoch: 3 -- Step: 4890 -- d_loss: 0.9879447817802429 -- g_loss: 1.0637470483779907
Epoch: 3 -- Step: 4900 -- d_loss: 0.9881429672241211 -- g_loss: 0.9730182886123657
Epoch: 3 -- Step: 4910 -- d_loss: 1.323712944984436 -- g_loss: 0.47481364011764526
Epoch: 3 -- Step: 4920 -- d_loss: 1.254725694656372 -- g_loss: 0.7404286861419678
Epoch: 3 -- Step: 4930 -- d_loss: 0.9356290102005005 -- g_loss: 1.044600248336792
Epoch: 3 -- Step: 4940 -- d_loss: 1.3893543481826782 -- g_loss: 1.7570455074310303
Epoch: 3 -- Step: 4950 -- d_loss: 0.7158617973327637 -- g_loss: 1.0356918573379517
Epoch: 3 -- Step: 4960 -- d_loss: 1.1422092914581299 -- g_loss: 0.6765161752700806
Epoch: 3 -- Step: 4970 -- d_loss: 1.5222032070159912 -- g_loss: 0.3330411911010742
Epoch: 3 -- Step: 4980 -- d_loss: 0.9931207895278931 -- g_loss: 0.7070862054824829
Epoch: 3 -- Step: 4990 -- d_loss: 0.9255528450012207 -- g_loss: 1.1679362058639526
Epoch: 3 -- Step: 5000 -- d_loss: 1.0575222969055176 -- g_loss: 0.701143741607666
Epoch: 3 -- Step: 5010 -- d_loss: 0.8465750217437744 -- g_loss: 0.9840108156204224
Epoch: 3 -- Step: 5020 -- d_loss: 0.9356163740158081 -- g_loss: 0.7169027328491211
Epoch: 3 -- Step: 5030 -- d_loss: 1.2666337490081787 -- g_loss: 0.46268826723098755
Epoch: 3 -- Step: 5040 -- d_loss: 0.8951082825660706 -- g_loss: 0.7147142291069031
Epoch: 3 -- Step: 5050 -- d_loss: 0.6891434192657471 -- g_loss: 1.5255241394042969
Epoch: 3 -- Step: 5060 -- d_loss: 1.9152950048446655 -- g_loss: 0.22472533583641052
Epoch: 3 -- Step: 5070 -- d_loss: 0.905669093132019 -- g_loss: 0.7989577054977417
Epoch: 3 -- Step: 5080 -- d_loss: 2.153576374053955 -- g_loss: 2.0948665142059326
Epoch: 3 -- Step: 5090 -- d_loss: 1.3003357648849487 -- g_loss: 0.8477039337158203
Epoch: 3 -- Step: 5100 -- d_loss: 1.0931402444839478 -- g_loss: 0.7266299724578857
Epoch: 3 -- Step: 5110 -- d_loss: 0.3306162655353546 -- g_loss: 3.915597438812256
Epoch: 3 -- Step: 5120 -- d_loss: 1.2048698663711548 -- g_loss: 0.48473942279815674
Epoch: 3 -- Step: 5130 -- d_loss: 0.45854753255844116 -- g_loss: 2.159616470336914
Epoch: 3 -- Step: 5140 -- d_loss: 0.535489559173584 -- g_loss: 1.4881104230880737
Epoch: 3 -- Step: 5150 -- d_loss: 1.394092321395874 -- g_loss: 0.39434757828712463
Epoch: 3 -- Step: 5160 -- d_loss: 0.4155859351158142 -- g_loss: 2.5711827278137207
Epoch: 3 -- Step: 5170 -- d_loss: 1.1925657987594604 -- g_loss: 0.5772310495376587
Epoch: 3 -- Step: 5180 -- d_loss: 0.9950437545776367 -- g_loss: 0.7712680101394653
Epoch: 3 -- Step: 5190 -- d_loss: 1.0699068307876587 -- g_loss: 0.7254975438117981
Epoch: 3 -- Step: 5200 -- d_loss: 0.8853362798690796 -- g_loss: 1.0651769638061523
Epoch: 3 -- Step: 5210 -- d_loss: 0.38721758127212524 -- g_loss: 2.23933744430542
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Epoch: 3 -- Step: 5230 -- d_loss: 0.6753207445144653 -- g_loss: 1.5848042964935303
Epoch: 3 -- Step: 5240 -- d_loss: 0.6335067749023438 -- g_loss: 1.2648394107818604
Epoch: 3 -- Step: 5250 -- d_loss: 1.5463643074035645 -- g_loss: 0.3061347007751465
Epoch: 3 -- Step: 5260 -- d_loss: 1.0519863367080688 -- g_loss: 0.6431902050971985
Epoch: 3 -- Step: 5270 -- d_loss: 1.1261370182037354 -- g_loss: 0.5507720708847046
Epoch: 3 -- Step: 5280 -- d_loss: 1.195791244506836 -- g_loss: 0.49632689356803894
Epoch: 3 -- Step: 5290 -- d_loss: 0.9410687685012817 -- g_loss: 1.474910020828247
Epoch: 3 -- Step: 5300 -- d_loss: 1.216943383216858 -- g_loss: 0.5124677419662476
Epoch: 3 -- Step: 5310 -- d_loss: 0.47983768582344055 -- g_loss: 2.77785062789917
Epoch: 3 -- Step: 5320 -- d_loss: 1.4223164319992065 -- g_loss: 0.4083544611930847
Epoch: 3 -- Step: 5330 -- d_loss: 1.205836534500122 -- g_loss: 0.7588340044021606
Epoch: 3 -- Step: 5340 -- d_loss: 0.33407995104789734 -- g_loss: 2.261955738067627
Epoch: 3 -- Step: 5350 -- d_loss: 1.0522369146347046 -- g_loss: 0.6875384449958801
Epoch: 3 -- Step: 5360 -- d_loss: 0.46150171756744385 -- g_loss: 2.317592144012451
Epoch: 3 -- Step: 5370 -- d_loss: 1.932619571685791 -- g_loss: 2.6070380210876465
Epoch: 3 -- Step: 5380 -- d_loss: 1.283249855041504 -- g_loss: 0.47476181387901306
Epoch: 3 -- Step: 5390 -- d_loss: 1.2117061614990234 -- g_loss: 0.5282039642333984
Epoch: 3 -- Step: 5400 -- d_loss: 0.8087238073348999 -- g_loss: 1.075230360031128
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Epoch: 3 -- Step: 5430 -- d_loss: 0.5746219158172607 -- g_loss: 1.4071643352508545
Epoch: 3 -- Step: 5440 -- d_loss: 1.2612725496292114 -- g_loss: 0.5770018696784973
Epoch: 3 -- Step: 5450 -- d_loss: 1.022613525390625 -- g_loss: 0.7680991291999817
Epoch: 3 -- Step: 5460 -- d_loss: 1.1207033395767212 -- g_loss: 0.6751740574836731
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Epoch: 3 -- Step: 5480 -- d_loss: 0.7736544013023376 -- g_loss: 1.432891607284546
Epoch: 3 -- Step: 5490 -- d_loss: 0.5073819160461426 -- g_loss: 1.3797824382781982
Epoch: 3 -- Step: 5500 -- d_loss: 0.6508986949920654 -- g_loss: 1.979662537574768
Epoch: 3 -- Step: 5510 -- d_loss: 1.2963377237319946 -- g_loss: 0.6999146938323975
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Epoch: 3 -- Step: 5530 -- d_loss: 1.1404368877410889 -- g_loss: 0.6195816993713379
Epoch: 3 -- Step: 5540 -- d_loss: 1.5668585300445557 -- g_loss: 2.4167017936706543
Epoch: 3 -- Step: 5550 -- d_loss: 0.8507634401321411 -- g_loss: 0.9086087942123413
Epoch: 3 -- Step: 5560 -- d_loss: 0.8652869462966919 -- g_loss: 1.6014868021011353
Epoch: 3 -- Step: 5570 -- d_loss: 0.9826459884643555 -- g_loss: 0.9910964965820312
Epoch: 3 -- Step: 5580 -- d_loss: 1.49909508228302 -- g_loss: 0.3357955813407898
Epoch: 3 -- Step: 5590 -- d_loss: 1.0451278686523438 -- g_loss: 0.7956720590591431
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Epoch: 3 -- Step: 5610 -- d_loss: 1.3974528312683105 -- g_loss: 0.39043712615966797
Epoch: 3 -- Step: 5620 -- d_loss: 0.9441946744918823 -- g_loss: 1.092693567276001
Epoch: 3 -- Step: 5630 -- d_loss: 0.9428244233131409 -- g_loss: 0.9428096413612366
Epoch: 3 -- Step: 5640 -- d_loss: 0.8812214136123657 -- g_loss: 0.9653010368347168
Epoch: 3 -- Step: 5650 -- d_loss: 0.8037968873977661 -- g_loss: 1.0642467737197876
Epoch: 3 -- Step: 5660 -- d_loss: 1.0012836456298828 -- g_loss: 1.1109740734100342
Epoch: 3 -- Step: 5670 -- d_loss: 1.103225827217102 -- g_loss: 0.6877787709236145
Epoch: 3 -- Step: 5680 -- d_loss: 0.6385979652404785 -- g_loss: 1.1690053939819336
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Epoch: 3 -- Step: 5700 -- d_loss: 1.1723442077636719 -- g_loss: 1.3603415489196777
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Epoch: 3 -- Step: 5790 -- d_loss: 0.9889053106307983 -- g_loss: 1.4000513553619385
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Epoch: 3 -- Step: 5830 -- d_loss: 1.054758906364441 -- g_loss: 0.8248740434646606
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Epoch: 3 -- Step: 5850 -- d_loss: 1.6900285482406616 -- g_loss: 0.28188198804855347
Epoch: 3 -- Step: 5860 -- d_loss: 1.1439521312713623 -- g_loss: 1.6338670253753662
Epoch: 3 -- Step: 5870 -- d_loss: 1.220057487487793 -- g_loss: 0.5152255892753601
Epoch: 3 -- Step: 5880 -- d_loss: 1.1211826801300049 -- g_loss: 1.581239938735962
Epoch: 3 -- Step: 5890 -- d_loss: 1.5361520051956177 -- g_loss: 0.3290800154209137
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Epoch: 3 -- Step: 5910 -- d_loss: 0.987594723701477 -- g_loss: 0.7717670202255249
Epoch: 3 -- Step: 5920 -- d_loss: 1.3477517366409302 -- g_loss: 0.4304496645927429
Epoch: 3 -- Step: 5930 -- d_loss: 1.0007368326187134 -- g_loss: 1.0014270544052124
Epoch: 3 -- Step: 5940 -- d_loss: 1.2800774574279785 -- g_loss: 0.6205130815505981
Epoch: 3 -- Step: 5950 -- d_loss: 0.6304811239242554 -- g_loss: 1.7546133995056152
Epoch: 3 -- Step: 5960 -- d_loss: 0.7225198745727539 -- g_loss: 1.3087315559387207
Epoch: 3 -- Step: 5970 -- d_loss: 0.37634268403053284 -- g_loss: 3.9841160774230957
Epoch: 3 -- Step: 5980 -- d_loss: 0.3474220633506775 -- g_loss: 1.6820133924484253
Epoch: 3 -- Step: 5990 -- d_loss: 1.0591250658035278 -- g_loss: 0.5508956909179688
Epoch: 3 -- Step: 6000 -- d_loss: 0.29962158203125 -- g_loss: 2.491875648498535
Epoch: 3 -- Step: 6010 -- d_loss: 0.5620837807655334 -- g_loss: 1.641538143157959
Epoch: 3 -- Step: 6020 -- d_loss: 1.1683261394500732 -- g_loss: 0.529352068901062
Epoch: 3 -- Step: 6030 -- d_loss: 1.1865274906158447 -- g_loss: 0.6815805435180664
Epoch: 3 -- Step: 6040 -- d_loss: 1.1640112400054932 -- g_loss: 1.0597326755523682
Epoch: 3 -- Step: 6050 -- d_loss: 1.861109972000122 -- g_loss: 3.2512502670288086
Epoch: 3 -- Step: 6060 -- d_loss: 2.398837089538574 -- g_loss: 2.5196309089660645
Epoch: 3 -- Step: 6070 -- d_loss: 1.3611853122711182 -- g_loss: 0.8159546852111816
Epoch: 3 -- Step: 6080 -- d_loss: 1.1365385055541992 -- g_loss: 0.708412766456604
Epoch: 3 -- Step: 6090 -- d_loss: 1.3540856838226318 -- g_loss: 0.5300081372261047
Epoch: 3 -- Step: 6100 -- d_loss: 0.3131251335144043 -- g_loss: 3.15681791305542
Epoch: 3 -- Step: 6110 -- d_loss: 0.7792664170265198 -- g_loss: 1.0164389610290527
Epoch: 3 -- Step: 6120 -- d_loss: 1.1833986043930054 -- g_loss: 0.493785560131073
Epoch: 3 -- Step: 6130 -- d_loss: 1.0727019309997559 -- g_loss: 0.6580410003662109
Epoch: 3 -- Step: 6140 -- d_loss: 1.8746691942214966 -- g_loss: 0.21639952063560486
Epoch: 3 -- Step: 6150 -- d_loss: 1.2793294191360474 -- g_loss: 0.564586877822876
Epoch: 3 -- Step: 6160 -- d_loss: 1.1175563335418701 -- g_loss: 0.6367015242576599
Epoch: 3 -- Step: 6170 -- d_loss: 1.7195631265640259 -- g_loss: 0.27366286516189575
Epoch: 3 -- Step: 6180 -- d_loss: 1.0432626008987427 -- g_loss: 0.8812515735626221
Epoch: 3 -- Step: 6190 -- d_loss: 1.094472050666809 -- g_loss: 0.6175071597099304
Epoch: 3 -- Step: 6200 -- d_loss: 1.5531095266342163 -- g_loss: 0.33468765020370483
Epoch: 3 -- Step: 6210 -- d_loss: 0.817779541015625 -- g_loss: 1.134703516960144
Epoch: 3 -- Step: 6220 -- d_loss: 0.9523108005523682 -- g_loss: 1.269548773765564
Epoch: 3 -- Step: 6230 -- d_loss: 1.2665038108825684 -- g_loss: 0.5797390937805176
Epoch: 3 -- Step: 6240 -- d_loss: 1.2864046096801758 -- g_loss: 0.4772995710372925
Epoch: 3 -- Step: 6250 -- d_loss: 1.374794602394104 -- g_loss: 0.49750879406929016
Epoch: 3 -- Step: 6260 -- d_loss: 1.0339537858963013 -- g_loss: 0.7902674674987793
Epoch: 3 -- Step: 6270 -- d_loss: 1.35903799533844 -- g_loss: 0.41644877195358276
Epoch: 3 -- Step: 6280 -- d_loss: 1.1293046474456787 -- g_loss: 0.6605925559997559
Epoch: 3 -- Step: 6290 -- d_loss: 1.1547636985778809 -- g_loss: 2.328793525695801
Epoch: 3 -- Step: 6300 -- d_loss: 1.1299505233764648 -- g_loss: 0.5374144315719604
Epoch: 3 -- Step: 6310 -- d_loss: 0.5068479776382446 -- g_loss: 1.520826816558838
Epoch: 3 -- Step: 6320 -- d_loss: 1.5659478902816772 -- g_loss: 0.30547478795051575
Epoch: 4 -- Step: 6330 -- d_loss: 1.1507902145385742 -- g_loss: 0.8064966797828674
Epoch: 4 -- Step: 6340 -- d_loss: 1.7655692100524902 -- g_loss: 0.3084558844566345
Epoch: 4 -- Step: 6350 -- d_loss: 1.0405657291412354 -- g_loss: 1.8480496406555176
Epoch: 4 -- Step: 6360 -- d_loss: 0.6984837055206299 -- g_loss: 1.9873138666152954
Epoch: 4 -- Step: 6370 -- d_loss: 1.379252552986145 -- g_loss: 0.38198959827423096
Epoch: 4 -- Step: 6380 -- d_loss: 0.7334651947021484 -- g_loss: 1.1100900173187256
Epoch: 4 -- Step: 6390 -- d_loss: 0.824618935585022 -- g_loss: 0.9816322326660156
Epoch: 4 -- Step: 6400 -- d_loss: 1.1599431037902832 -- g_loss: 0.6826377511024475
Epoch: 4 -- Step: 6410 -- d_loss: 1.19020414352417 -- g_loss: 1.1428515911102295
Epoch: 4 -- Step: 6420 -- d_loss: 1.2823481559753418 -- g_loss: 0.9397339820861816
Epoch: 4 -- Step: 6430 -- d_loss: 1.668558120727539 -- g_loss: 0.2896915376186371
Epoch: 4 -- Step: 6440 -- d_loss: 1.1529167890548706 -- g_loss: 0.8236013054847717
Epoch: 4 -- Step: 6450 -- d_loss: 1.1308892965316772 -- g_loss: 1.2427459955215454
Epoch: 4 -- Step: 6460 -- d_loss: 0.9845600724220276 -- g_loss: 0.9431906938552856
Epoch: 4 -- Step: 6470 -- d_loss: 1.4573497772216797 -- g_loss: 0.3776877224445343
Epoch: 4 -- Step: 6480 -- d_loss: 1.0525286197662354 -- g_loss: 1.268998146057129
Epoch: 4 -- Step: 6490 -- d_loss: 1.1587775945663452 -- g_loss: 0.5428244471549988
Epoch: 4 -- Step: 6500 -- d_loss: 1.0930140018463135 -- g_loss: 1.2268633842468262
Epoch: 4 -- Step: 6510 -- d_loss: 1.528927206993103 -- g_loss: 0.3179084062576294
Epoch: 4 -- Step: 6520 -- d_loss: 1.053758978843689 -- g_loss: 0.776018500328064
Epoch: 4 -- Step: 6530 -- d_loss: 1.5615414381027222 -- g_loss: 0.3314703106880188
Epoch: 4 -- Step: 6540 -- d_loss: 1.0432255268096924 -- g_loss: 0.8143783211708069
Epoch: 4 -- Step: 6550 -- d_loss: 1.1087183952331543 -- g_loss: 0.9568529725074768
Epoch: 4 -- Step: 6560 -- d_loss: 1.1164079904556274 -- g_loss: 0.8750157356262207
Epoch: 4 -- Step: 6570 -- d_loss: 1.0096138715744019 -- g_loss: 2.150491714477539
Epoch: 4 -- Step: 6580 -- d_loss: 1.052868127822876 -- g_loss: 1.4616501331329346
Epoch: 4 -- Step: 6590 -- d_loss: 0.91474848985672 -- g_loss: 0.9869033098220825
Epoch: 4 -- Step: 6600 -- d_loss: 1.0102543830871582 -- g_loss: 1.743870735168457
Epoch: 4 -- Step: 6610 -- d_loss: 1.1144073009490967 -- g_loss: 0.6447474956512451
Epoch: 4 -- Step: 6620 -- d_loss: 0.8759568929672241 -- g_loss: 1.3015265464782715
Epoch: 4 -- Step: 6630 -- d_loss: 1.5949208736419678 -- g_loss: 0.324055016040802
Epoch: 4 -- Step: 6640 -- d_loss: 1.1661083698272705 -- g_loss: 0.7961821556091309
Epoch: 4 -- Step: 6650 -- d_loss: 1.1754796504974365 -- g_loss: 1.4253476858139038
Epoch: 4 -- Step: 6660 -- d_loss: 0.6433048248291016 -- g_loss: 1.4644243717193604
Epoch: 4 -- Step: 6670 -- d_loss: 1.2989585399627686 -- g_loss: 0.5608990788459778
Epoch: 4 -- Step: 6680 -- d_loss: 1.2074685096740723 -- g_loss: 1.454014539718628
Epoch: 4 -- Step: 6690 -- d_loss: 0.9593032598495483 -- g_loss: 1.0990207195281982
Epoch: 4 -- Step: 6700 -- d_loss: 0.9055099487304688 -- g_loss: 0.9634027481079102
Epoch: 4 -- Step: 6710 -- d_loss: 0.8647861480712891 -- g_loss: 1.2387428283691406
Epoch: 4 -- Step: 6720 -- d_loss: 1.3772281408309937 -- g_loss: 0.4274836778640747
Epoch: 4 -- Step: 6730 -- d_loss: 1.3444311618804932 -- g_loss: 0.41149652004241943
Epoch: 4 -- Step: 6740 -- d_loss: 0.8608455657958984 -- g_loss: 0.8417161703109741
Epoch: 4 -- Step: 6750 -- d_loss: 1.6831556558609009 -- g_loss: 0.28067681193351746
Epoch: 4 -- Step: 6760 -- d_loss: 1.1642847061157227 -- g_loss: 1.477705717086792
Epoch: 4 -- Step: 6770 -- d_loss: 1.6286925077438354 -- g_loss: 0.29492461681365967
Epoch: 4 -- Step: 6780 -- d_loss: 0.7269939184188843 -- g_loss: 1.3698382377624512
Epoch: 4 -- Step: 6790 -- d_loss: 0.7512883543968201 -- g_loss: 1.7134040594100952
Epoch: 4 -- Step: 6800 -- d_loss: 1.1381525993347168 -- g_loss: 0.7964102029800415
Epoch: 4 -- Step: 6810 -- d_loss: 1.5669283866882324 -- g_loss: 0.3142123520374298
Epoch: 4 -- Step: 6820 -- d_loss: 0.5648057460784912 -- g_loss: 1.6558618545532227
Epoch: 4 -- Step: 6830 -- d_loss: 0.7052483558654785 -- g_loss: 1.312842607498169
Epoch: 4 -- Step: 6840 -- d_loss: 0.515636682510376 -- g_loss: 1.4204672574996948
Epoch: 4 -- Step: 6850 -- d_loss: 0.874973714351654 -- g_loss: 1.033322811126709
Epoch: 4 -- Step: 6860 -- d_loss: 1.0840938091278076 -- g_loss: 1.1520302295684814
Epoch: 4 -- Step: 6870 -- d_loss: 1.0898313522338867 -- g_loss: 0.7989075183868408
Epoch: 4 -- Step: 6880 -- d_loss: 1.4087377786636353 -- g_loss: 0.41975775361061096
Epoch: 4 -- Step: 6890 -- d_loss: 1.288455843925476 -- g_loss: 0.516065239906311
Epoch: 4 -- Step: 6900 -- d_loss: 0.9774879813194275 -- g_loss: 0.8594238758087158
Epoch: 4 -- Step: 6910 -- d_loss: 1.1877319812774658 -- g_loss: 0.7168443202972412
Epoch: 4 -- Step: 6920 -- d_loss: 1.0646312236785889 -- g_loss: 0.986520528793335
Epoch: 4 -- Step: 6930 -- d_loss: 1.1306235790252686 -- g_loss: 0.8950362205505371
Epoch: 4 -- Step: 6940 -- d_loss: 1.0871754884719849 -- g_loss: 1.090927004814148
Epoch: 4 -- Step: 6950 -- d_loss: 1.0317471027374268 -- g_loss: 0.7152509689331055
Epoch: 4 -- Step: 6960 -- d_loss: 1.6380934715270996 -- g_loss: 0.28355735540390015
Epoch: 4 -- Step: 6970 -- d_loss: 1.1538770198822021 -- g_loss: 0.5519386529922485
Epoch: 4 -- Step: 6980 -- d_loss: 1.244994878768921 -- g_loss: 0.5136693716049194
Epoch: 4 -- Step: 6990 -- d_loss: 1.0029064416885376 -- g_loss: 0.7128620147705078
Epoch: 4 -- Step: 7000 -- d_loss: 0.33774152398109436 -- g_loss: 2.1404380798339844
Epoch: 4 -- Step: 7010 -- d_loss: 0.9426360130310059 -- g_loss: 0.7069437503814697
Epoch: 4 -- Step: 7020 -- d_loss: 1.5648975372314453 -- g_loss: 0.32003021240234375
Epoch: 4 -- Step: 7030 -- d_loss: 0.9963889718055725 -- g_loss: 0.8474361896514893
Epoch: 4 -- Step: 7040 -- d_loss: 1.155958652496338 -- g_loss: 0.683208703994751
Epoch: 4 -- Step: 7050 -- d_loss: 1.479364275932312 -- g_loss: 1.115951657295227
Epoch: 4 -- Step: 7060 -- d_loss: 0.8583966493606567 -- g_loss: 1.445767879486084
Epoch: 4 -- Step: 7070 -- d_loss: 0.8586779236793518 -- g_loss: 1.0029873847961426
Epoch: 4 -- Step: 7080 -- d_loss: 1.2019691467285156 -- g_loss: 0.489890456199646
Epoch: 4 -- Step: 7090 -- d_loss: 1.1362007856369019 -- g_loss: 0.6944493055343628
Epoch: 4 -- Step: 7100 -- d_loss: 0.7245704531669617 -- g_loss: 1.348116397857666
Epoch: 4 -- Step: 7110 -- d_loss: 1.084902048110962 -- g_loss: 0.9071055054664612
Epoch: 4 -- Step: 7120 -- d_loss: 1.09089994430542 -- g_loss: 0.9319583177566528
Epoch: 4 -- Step: 7130 -- d_loss: 1.0566997528076172 -- g_loss: 1.0286216735839844
Epoch: 4 -- Step: 7140 -- d_loss: 1.0856260061264038 -- g_loss: 1.5998518466949463
Epoch: 4 -- Step: 7150 -- d_loss: 0.9087430238723755 -- g_loss: 0.8341672420501709
Epoch: 4 -- Step: 7160 -- d_loss: 1.1874655485153198 -- g_loss: 0.5313106775283813
Epoch: 4 -- Step: 7170 -- d_loss: 1.159005880355835 -- g_loss: 0.5343914031982422
Epoch: 4 -- Step: 7180 -- d_loss: 1.0175485610961914 -- g_loss: 0.6560325622558594
Epoch: 4 -- Step: 7190 -- d_loss: 1.0323066711425781 -- g_loss: 0.6970677971839905
Epoch: 4 -- Step: 7200 -- d_loss: 1.1281816959381104 -- g_loss: 0.6001434326171875
Epoch: 4 -- Step: 7210 -- d_loss: 1.3815053701400757 -- g_loss: 1.8124009370803833
Epoch: 4 -- Step: 7220 -- d_loss: 1.0694975852966309 -- g_loss: 0.7963610887527466
Epoch: 4 -- Step: 7230 -- d_loss: 0.998275876045227 -- g_loss: 0.8084712028503418
Epoch: 4 -- Step: 7240 -- d_loss: 0.7795863151550293 -- g_loss: 1.002318024635315
Epoch: 4 -- Step: 7250 -- d_loss: 0.8579015731811523 -- g_loss: 0.8541088104248047
Epoch: 4 -- Step: 7260 -- d_loss: 0.5978658199310303 -- g_loss: 1.4510284662246704
Epoch: 4 -- Step: 7270 -- d_loss: 0.7584898471832275 -- g_loss: 1.1768805980682373
Epoch: 4 -- Step: 7280 -- d_loss: 0.7819344997406006 -- g_loss: 0.8445367217063904
Epoch: 4 -- Step: 7290 -- d_loss: 1.0007566213607788 -- g_loss: 0.7694944143295288
Epoch: 4 -- Step: 7300 -- d_loss: 1.1961441040039062 -- g_loss: 0.6076934337615967
Epoch: 4 -- Step: 7310 -- d_loss: 1.1118074655532837 -- g_loss: 0.6290270090103149
Epoch: 4 -- Step: 7320 -- d_loss: 1.4356951713562012 -- g_loss: 0.38518208265304565
Epoch: 4 -- Step: 7330 -- d_loss: 1.3885180950164795 -- g_loss: 1.0664174556732178
Epoch: 4 -- Step: 7340 -- d_loss: 1.1369709968566895 -- g_loss: 0.8908944725990295
Epoch: 4 -- Step: 7350 -- d_loss: 1.1891331672668457 -- g_loss: 0.8395596146583557
Epoch: 4 -- Step: 7360 -- d_loss: 1.1635191440582275 -- g_loss: 0.5212569832801819
Epoch: 4 -- Step: 7370 -- d_loss: 1.078273057937622 -- g_loss: 0.8006534576416016
Epoch: 4 -- Step: 7380 -- d_loss: 1.0554537773132324 -- g_loss: 0.6930011510848999
Epoch: 4 -- Step: 7390 -- d_loss: 0.9393492937088013 -- g_loss: 0.8728691935539246
Epoch: 4 -- Step: 7400 -- d_loss: 1.1780645847320557 -- g_loss: 0.6064751744270325
Epoch: 4 -- Step: 7410 -- d_loss: 0.8871992826461792 -- g_loss: 1.1573055982589722
Epoch: 4 -- Step: 7420 -- d_loss: 1.113737940788269 -- g_loss: 0.6585202217102051
Epoch: 4 -- Step: 7430 -- d_loss: 1.1036384105682373 -- g_loss: 0.6182050704956055
Epoch: 4 -- Step: 7440 -- d_loss: 1.0826059579849243 -- g_loss: 0.8236918449401855
Epoch: 4 -- Step: 7450 -- d_loss: 1.479203701019287 -- g_loss: 0.3512985408306122
Epoch: 4 -- Step: 7460 -- d_loss: 1.1770402193069458 -- g_loss: 0.5293409824371338
Epoch: 4 -- Step: 7470 -- d_loss: 0.9776275157928467 -- g_loss: 0.8218404650688171
Epoch: 4 -- Step: 7480 -- d_loss: 0.7037062644958496 -- g_loss: 1.5159318447113037
Epoch: 4 -- Step: 7490 -- d_loss: 0.9173691868782043 -- g_loss: 0.8440756797790527
Epoch: 4 -- Step: 7500 -- d_loss: 1.4653500318527222 -- g_loss: 0.4617666006088257
Epoch: 4 -- Step: 7510 -- d_loss: 1.3864275217056274 -- g_loss: 0.41479945182800293
Epoch: 4 -- Step: 7520 -- d_loss: 1.280174732208252 -- g_loss: 0.4838716685771942
Epoch: 4 -- Step: 7530 -- d_loss: 1.1998310089111328 -- g_loss: 1.1595897674560547
Epoch: 4 -- Step: 7540 -- d_loss: 0.9066548347473145 -- g_loss: 0.8710905313491821
Epoch: 4 -- Step: 7550 -- d_loss: 1.206557035446167 -- g_loss: 0.6805439591407776
Epoch: 4 -- Step: 7560 -- d_loss: 1.0765937566757202 -- g_loss: 0.7137876749038696
Epoch: 4 -- Step: 7570 -- d_loss: 1.467750906944275 -- g_loss: 1.704782247543335
Epoch: 4 -- Step: 7580 -- d_loss: 0.8609157800674438 -- g_loss: 0.893756628036499
Epoch: 4 -- Step: 7590 -- d_loss: 1.1551010608673096 -- g_loss: 0.7935304045677185
Epoch: 4 -- Step: 7600 -- d_loss: 1.4919685125350952 -- g_loss: 0.35704997181892395
Epoch: 4 -- Step: 7610 -- d_loss: 1.2562209367752075 -- g_loss: 0.5460571050643921
Epoch: 4 -- Step: 7620 -- d_loss: 1.1158180236816406 -- g_loss: 0.6173512935638428
Epoch: 4 -- Step: 7630 -- d_loss: 0.8231276869773865 -- g_loss: 1.08203125
Epoch: 4 -- Step: 7640 -- d_loss: 0.8355008959770203 -- g_loss: 0.9294366240501404
Epoch: 4 -- Step: 7650 -- d_loss: 1.2113842964172363 -- g_loss: 1.8644918203353882
Epoch: 4 -- Step: 7660 -- d_loss: 1.4791920185089111 -- g_loss: 0.3473885655403137
Epoch: 4 -- Step: 7670 -- d_loss: 1.5016947984695435 -- g_loss: 0.38949650526046753
Epoch: 4 -- Step: 7680 -- d_loss: 1.5875539779663086 -- g_loss: 0.322002649307251
Epoch: 4 -- Step: 7690 -- d_loss: 0.7893484234809875 -- g_loss: 1.468623161315918
Epoch: 4 -- Step: 7700 -- d_loss: 0.9735302329063416 -- g_loss: 1.298423171043396
Epoch: 4 -- Step: 7710 -- d_loss: 1.1938923597335815 -- g_loss: 0.9464308023452759
Epoch: 4 -- Step: 7720 -- d_loss: 1.2393274307250977 -- g_loss: 0.4908865690231323
Epoch: 4 -- Step: 7730 -- d_loss: 0.2558801770210266 -- g_loss: 3.5181570053100586
Epoch: 4 -- Step: 7740 -- d_loss: 0.5210274457931519 -- g_loss: 1.3715529441833496
Epoch: 4 -- Step: 7750 -- d_loss: 0.9750124216079712 -- g_loss: 1.0355167388916016
Epoch: 4 -- Step: 7760 -- d_loss: 1.2236582040786743 -- g_loss: 0.485474169254303
Epoch: 4 -- Step: 7770 -- d_loss: 0.7754720449447632 -- g_loss: 1.0419594049453735
Epoch: 4 -- Step: 7780 -- d_loss: 1.291416883468628 -- g_loss: 0.4504387378692627
Epoch: 4 -- Step: 7790 -- d_loss: 1.1622203588485718 -- g_loss: 0.6871954798698425
Epoch: 4 -- Step: 7800 -- d_loss: 1.2283098697662354 -- g_loss: 1.5943248271942139
Epoch: 4 -- Step: 7810 -- d_loss: 1.291853666305542 -- g_loss: 0.4499698281288147
Epoch: 4 -- Step: 7820 -- d_loss: 1.2510967254638672 -- g_loss: 0.5403247475624084
Epoch: 4 -- Step: 7830 -- d_loss: 1.4801607131958008 -- g_loss: 0.37929120659828186
Epoch: 4 -- Step: 7840 -- d_loss: 0.8077278137207031 -- g_loss: 1.271661400794983
Epoch: 4 -- Step: 7850 -- d_loss: 1.602139949798584 -- g_loss: 0.32352161407470703
Epoch: 4 -- Step: 7860 -- d_loss: 0.8283176422119141 -- g_loss: 1.2728086709976196
Epoch: 4 -- Step: 7870 -- d_loss: 1.3134785890579224 -- g_loss: 0.4529978036880493
Epoch: 4 -- Step: 7880 -- d_loss: 1.044832706451416 -- g_loss: 0.6644563674926758
Epoch: 4 -- Step: 7890 -- d_loss: 1.3162531852722168 -- g_loss: 0.47099965810775757
Epoch: 4 -- Step: 7900 -- d_loss: 1.246850609779358 -- g_loss: 0.6849435567855835
Epoch: 4 -- Step: 7910 -- d_loss: 0.6022448539733887 -- g_loss: 1.6755791902542114
Epoch: 5 -- Step: 7920 -- d_loss: 1.4099805355072021 -- g_loss: 0.5786327123641968
Epoch: 5 -- Step: 7930 -- d_loss: 0.9823029041290283 -- g_loss: 0.9400027990341187
Epoch: 5 -- Step: 7940 -- d_loss: 0.9186397790908813 -- g_loss: 0.8912849426269531
Epoch: 5 -- Step: 7950 -- d_loss: 0.9385233521461487 -- g_loss: 1.2221976518630981
Epoch: 5 -- Step: 7960 -- d_loss: 1.0490241050720215 -- g_loss: 0.7130724191665649
Epoch: 5 -- Step: 7970 -- d_loss: 1.0073671340942383 -- g_loss: 0.8322417140007019
Epoch: 5 -- Step: 7980 -- d_loss: 1.0134197473526 -- g_loss: 1.0375287532806396
Epoch: 5 -- Step: 7990 -- d_loss: 0.8033446669578552 -- g_loss: 1.3120187520980835
Epoch: 5 -- Step: 8000 -- d_loss: 1.3636492490768433 -- g_loss: 0.41580331325531006
Epoch: 5 -- Step: 8010 -- d_loss: 1.1875507831573486 -- g_loss: 0.6834365129470825
Epoch: 5 -- Step: 8020 -- d_loss: 1.0203593969345093 -- g_loss: 0.7215545773506165
Epoch: 5 -- Step: 8030 -- d_loss: 0.5897991061210632 -- g_loss: 1.2425589561462402
Epoch: 5 -- Step: 8040 -- d_loss: 0.9177379608154297 -- g_loss: 1.278610110282898
Epoch: 5 -- Step: 8050 -- d_loss: 1.5457810163497925 -- g_loss: 0.9775320291519165
Epoch: 5 -- Step: 8060 -- d_loss: 1.0055932998657227 -- g_loss: 1.1182734966278076
Epoch: 5 -- Step: 8070 -- d_loss: 0.8627122640609741 -- g_loss: 0.9629484415054321
Epoch: 5 -- Step: 8080 -- d_loss: 0.9727851152420044 -- g_loss: 1.1777740716934204
Epoch: 5 -- Step: 8090 -- d_loss: 0.9298640489578247 -- g_loss: 0.7770636081695557
Epoch: 5 -- Step: 8100 -- d_loss: 0.8106931447982788 -- g_loss: 0.9051065444946289
Epoch: 5 -- Step: 8110 -- d_loss: 1.1521517038345337 -- g_loss: 0.5998618602752686
Epoch: 5 -- Step: 8120 -- d_loss: 1.1865894794464111 -- g_loss: 1.3572262525558472
Epoch: 5 -- Step: 8130 -- d_loss: 0.9685280323028564 -- g_loss: 0.8315102458000183
Epoch: 5 -- Step: 8140 -- d_loss: 1.381431221961975 -- g_loss: 0.4250659942626953
Epoch: 5 -- Step: 8150 -- d_loss: 1.1555798053741455 -- g_loss: 0.6401737928390503
Epoch: 5 -- Step: 8160 -- d_loss: 1.6263253688812256 -- g_loss: 0.3288499116897583
Epoch: 5 -- Step: 8170 -- d_loss: 1.421604871749878 -- g_loss: 0.4007619023323059
Epoch: 5 -- Step: 8180 -- d_loss: 1.1562546491622925 -- g_loss: 0.5597179532051086
Epoch: 5 -- Step: 8190 -- d_loss: 0.9638112187385559 -- g_loss: 1.4794727563858032
Epoch: 5 -- Step: 8200 -- d_loss: 1.7418925762176514 -- g_loss: 0.279845654964447
Epoch: 5 -- Step: 8210 -- d_loss: 1.1942877769470215 -- g_loss: 0.6925122737884521
Epoch: 5 -- Step: 8220 -- d_loss: 1.177901029586792 -- g_loss: 0.6937172412872314
Epoch: 5 -- Step: 8230 -- d_loss: 1.0672053098678589 -- g_loss: 0.8280134797096252
Epoch: 5 -- Step: 8240 -- d_loss: 1.267927885055542 -- g_loss: 0.547391414642334
Epoch: 5 -- Step: 8250 -- d_loss: 1.0659193992614746 -- g_loss: 0.6845631003379822
Epoch: 5 -- Step: 8260 -- d_loss: 2.787950038909912 -- g_loss: 0.09443259239196777
Epoch: 5 -- Step: 8270 -- d_loss: 1.3621983528137207 -- g_loss: 0.45838385820388794
Epoch: 5 -- Step: 8280 -- d_loss: 1.209120273590088 -- g_loss: 0.7387605309486389
Epoch: 5 -- Step: 8290 -- d_loss: 0.7626805305480957 -- g_loss: 1.1308631896972656
Epoch: 5 -- Step: 8300 -- d_loss: 1.0669264793395996 -- g_loss: 0.7781090140342712
Epoch: 5 -- Step: 8310 -- d_loss: 1.3435921669006348 -- g_loss: 0.44047263264656067
Epoch: 5 -- Step: 8320 -- d_loss: 1.2590724229812622 -- g_loss: 0.48454996943473816
Epoch: 5 -- Step: 8330 -- d_loss: 1.8264691829681396 -- g_loss: 0.25679972767829895
Epoch: 5 -- Step: 8340 -- d_loss: 1.0005537271499634 -- g_loss: 0.9298606514930725
Epoch: 5 -- Step: 8350 -- d_loss: 1.1849334239959717 -- g_loss: 0.5378950834274292
Epoch: 5 -- Step: 8360 -- d_loss: 0.47396793961524963 -- g_loss: 1.486276626586914
Epoch: 5 -- Step: 8370 -- d_loss: 1.0077818632125854 -- g_loss: 0.6417669653892517
Epoch: 5 -- Step: 8380 -- d_loss: 0.9277564287185669 -- g_loss: 0.7935541868209839
Epoch: 5 -- Step: 8390 -- d_loss: 1.1480708122253418 -- g_loss: 0.659305214881897
Epoch: 5 -- Step: 8400 -- d_loss: 1.0601954460144043 -- g_loss: 1.8524664640426636
Epoch: 5 -- Step: 8410 -- d_loss: 1.3001000881195068 -- g_loss: 1.291379451751709
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Epoch: 5 -- Step: 8460 -- d_loss: 1.0123376846313477 -- g_loss: 0.659896731376648
Epoch: 5 -- Step: 8470 -- d_loss: 1.0995876789093018 -- g_loss: 0.7274157404899597
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Epoch: 5 -- Step: 8500 -- d_loss: 1.4141075611114502 -- g_loss: 0.41076821088790894
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Epoch: 5 -- Step: 8590 -- d_loss: 1.5951781272888184 -- g_loss: 0.3501206636428833
Epoch: 5 -- Step: 8600 -- d_loss: 1.1848626136779785 -- g_loss: 0.5771867036819458
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Epoch: 5 -- Step: 8860 -- d_loss: 1.1153233051300049 -- g_loss: 0.7344145774841309
Epoch: 5 -- Step: 8870 -- d_loss: 1.2272319793701172 -- g_loss: 0.7919542789459229
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Epoch: 5 -- Step: 8890 -- d_loss: 1.1387451887130737 -- g_loss: 0.6776858568191528
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Epoch: 5 -- Step: 8970 -- d_loss: 0.9662722945213318 -- g_loss: 0.9268842339515686
Epoch: 5 -- Step: 8980 -- d_loss: 0.9806637763977051 -- g_loss: 1.468684196472168
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Epoch: 5 -- Step: 9050 -- d_loss: 1.0880074501037598 -- g_loss: 0.7362834811210632
Epoch: 5 -- Step: 9060 -- d_loss: 1.0719456672668457 -- g_loss: 0.6881409287452698
Epoch: 5 -- Step: 9070 -- d_loss: 1.1359808444976807 -- g_loss: 0.7056482434272766
Epoch: 5 -- Step: 9080 -- d_loss: 0.9792712926864624 -- g_loss: 1.1108964681625366
Epoch: 5 -- Step: 9090 -- d_loss: 0.9004079103469849 -- g_loss: 0.808344304561615
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Epoch: 5 -- Step: 9240 -- d_loss: 1.090395450592041 -- g_loss: 0.6673893928527832
Epoch: 5 -- Step: 9250 -- d_loss: 0.8517144322395325 -- g_loss: 0.8414717316627502
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Epoch: 5 -- Step: 9280 -- d_loss: 1.8865562677383423 -- g_loss: 0.21492211520671844
Epoch: 5 -- Step: 9290 -- d_loss: 1.0312522649765015 -- g_loss: 0.8093420267105103
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Epoch: 6 -- Step: 9500 -- d_loss: 1.0367987155914307 -- g_loss: 1.4001810550689697
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Epoch: 6 -- Step: 9540 -- d_loss: 1.109116792678833 -- g_loss: 0.9133995771408081
Epoch: 6 -- Step: 9550 -- d_loss: 0.45571351051330566 -- g_loss: 1.881594181060791
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Epoch: 6 -- Step: 9570 -- d_loss: 0.932131826877594 -- g_loss: 0.9153972864151001
Epoch: 6 -- Step: 9580 -- d_loss: 1.135466456413269 -- g_loss: 0.675335168838501
Epoch: 6 -- Step: 9590 -- d_loss: 1.0451213121414185 -- g_loss: 0.7735823392868042
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Epoch: 6 -- Step: 9630 -- d_loss: 1.0115681886672974 -- g_loss: 0.83366858959198
Epoch: 6 -- Step: 9640 -- d_loss: 0.858536422252655 -- g_loss: 0.993654727935791
Epoch: 6 -- Step: 9650 -- d_loss: 1.1882851123809814 -- g_loss: 0.9968938231468201
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Epoch: 6 -- Step: 9670 -- d_loss: 1.3237578868865967 -- g_loss: 0.4887056350708008
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Epoch: 6 -- Step: 9890 -- d_loss: 0.6598705053329468 -- g_loss: 1.2829421758651733
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Epoch: 6 -- Step: 10000 -- d_loss: 0.9950056076049805 -- g_loss: 0.7043527364730835
Epoch: 6 -- Step: 10010 -- d_loss: 1.6675777435302734 -- g_loss: 0.2979089617729187
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Epoch: 6 -- Step: 10030 -- d_loss: 0.9018085598945618 -- g_loss: 0.7727941870689392
Epoch: 6 -- Step: 10040 -- d_loss: 0.6137226819992065 -- g_loss: 1.58476984500885
Epoch: 6 -- Step: 10050 -- d_loss: 1.025289535522461 -- g_loss: 0.8959510326385498
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Epoch: 6 -- Step: 10070 -- d_loss: 0.703572154045105 -- g_loss: 1.4798001050949097
Epoch: 6 -- Step: 10080 -- d_loss: 0.8967299461364746 -- g_loss: 1.247682809829712
Epoch: 6 -- Step: 10090 -- d_loss: 1.504756212234497 -- g_loss: 0.39327478408813477
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Epoch: 6 -- Step: 10150 -- d_loss: 1.065199851989746 -- g_loss: 0.5796428918838501
Epoch: 6 -- Step: 10160 -- d_loss: 1.30765962600708 -- g_loss: 0.45317503809928894
Epoch: 6 -- Step: 10170 -- d_loss: 1.3251698017120361 -- g_loss: 0.41112303733825684
Epoch: 6 -- Step: 10180 -- d_loss: 1.2702600955963135 -- g_loss: 0.5432382225990295
Epoch: 6 -- Step: 10190 -- d_loss: 1.2205286026000977 -- g_loss: 0.6030615568161011
Epoch: 6 -- Step: 10200 -- d_loss: 1.6798484325408936 -- g_loss: 1.1837695837020874
Epoch: 6 -- Step: 10210 -- d_loss: 1.1516057252883911 -- g_loss: 1.1158742904663086
Epoch: 6 -- Step: 10220 -- d_loss: 1.153592586517334 -- g_loss: 0.6089180707931519
Epoch: 6 -- Step: 10230 -- d_loss: 0.9854596853256226 -- g_loss: 0.7100517153739929
Epoch: 6 -- Step: 10240 -- d_loss: 0.8525583744049072 -- g_loss: 0.8016816973686218
Epoch: 6 -- Step: 10250 -- d_loss: 0.7685941457748413 -- g_loss: 1.5437986850738525
Epoch: 6 -- Step: 10260 -- d_loss: 0.9508846998214722 -- g_loss: 1.3550212383270264
Epoch: 6 -- Step: 10270 -- d_loss: 1.329814076423645 -- g_loss: 0.4804441034793854
Epoch: 6 -- Step: 10280 -- d_loss: 1.4579845666885376 -- g_loss: 0.42394277453422546
Epoch: 6 -- Step: 10290 -- d_loss: 0.9866041541099548 -- g_loss: 1.2325440645217896
Epoch: 6 -- Step: 10300 -- d_loss: 1.1752134561538696 -- g_loss: 0.5533797144889832
Epoch: 6 -- Step: 10310 -- d_loss: 1.1798748970031738 -- g_loss: 0.9213124513626099
Epoch: 6 -- Step: 10320 -- d_loss: 0.9115083813667297 -- g_loss: 0.8056082725524902
Epoch: 6 -- Step: 10330 -- d_loss: 1.4427486658096313 -- g_loss: 0.4152190685272217
Epoch: 6 -- Step: 10340 -- d_loss: 0.916265606880188 -- g_loss: 0.9466313719749451
Epoch: 6 -- Step: 10350 -- d_loss: 0.8897536993026733 -- g_loss: 1.139754056930542
Epoch: 6 -- Step: 10360 -- d_loss: 1.2091238498687744 -- g_loss: 0.5461490750312805
Epoch: 6 -- Step: 10370 -- d_loss: 0.9638240337371826 -- g_loss: 1.1055642366409302
Epoch: 6 -- Step: 10380 -- d_loss: 1.2884776592254639 -- g_loss: 0.4450017809867859
Epoch: 6 -- Step: 10390 -- d_loss: 0.7364583611488342 -- g_loss: 1.0651804208755493
Epoch: 6 -- Step: 10400 -- d_loss: 1.1825419664382935 -- g_loss: 1.3189576864242554
Epoch: 6 -- Step: 10410 -- d_loss: 1.388170599937439 -- g_loss: 0.3794902265071869
Epoch: 6 -- Step: 10420 -- d_loss: 1.2028645277023315 -- g_loss: 0.7830238938331604
Epoch: 6 -- Step: 10430 -- d_loss: 1.0395467281341553 -- g_loss: 0.7430664300918579
Epoch: 6 -- Step: 10440 -- d_loss: 0.9458557963371277 -- g_loss: 0.7709637880325317
Epoch: 6 -- Step: 10450 -- d_loss: 0.9551182985305786 -- g_loss: 0.765087366104126
Epoch: 6 -- Step: 10460 -- d_loss: 1.3228728771209717 -- g_loss: 0.42839980125427246
Epoch: 6 -- Step: 10470 -- d_loss: 1.0512659549713135 -- g_loss: 0.6661204695701599
Epoch: 6 -- Step: 10480 -- d_loss: 1.1560959815979004 -- g_loss: 0.6176644563674927
Epoch: 6 -- Step: 10490 -- d_loss: 1.4932925701141357 -- g_loss: 0.40621042251586914
Epoch: 6 -- Step: 10500 -- d_loss: 1.7769616842269897 -- g_loss: 0.2531895339488983
Epoch: 6 -- Step: 10510 -- d_loss: 1.0290095806121826 -- g_loss: 0.8845224380493164
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Epoch: 6 -- Step: 10560 -- d_loss: 1.1592388153076172 -- g_loss: 0.7775955200195312
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Epoch: 6 -- Step: 10650 -- d_loss: 0.8861043453216553 -- g_loss: 0.949914276599884
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Epoch: 6 -- Step: 10790 -- d_loss: 0.8428019881248474 -- g_loss: 1.2967950105667114
Epoch: 6 -- Step: 10800 -- d_loss: 1.1640892028808594 -- g_loss: 0.716937243938446
Epoch: 6 -- Step: 10810 -- d_loss: 0.9354768991470337 -- g_loss: 0.7956169843673706
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Epoch: 6 -- Step: 10860 -- d_loss: 1.4890387058258057 -- g_loss: 0.3488501310348511
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Epoch: 6 -- Step: 10880 -- d_loss: 1.066452145576477 -- g_loss: 0.8096418380737305
Epoch: 6 -- Step: 10890 -- d_loss: 0.6955103874206543 -- g_loss: 1.3856829404830933
Epoch: 6 -- Step: 10900 -- d_loss: 1.1003448963165283 -- g_loss: 0.5715078115463257
Epoch: 6 -- Step: 10910 -- d_loss: 0.99664306640625 -- g_loss: 0.887243390083313
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Epoch: 6 -- Step: 10930 -- d_loss: 1.2380121946334839 -- g_loss: 0.669628381729126
Epoch: 6 -- Step: 10940 -- d_loss: 1.129908800125122 -- g_loss: 0.6960366368293762
Epoch: 6 -- Step: 10950 -- d_loss: 1.2721143960952759 -- g_loss: 0.4856571555137634
Epoch: 6 -- Step: 10960 -- d_loss: 1.018924355506897 -- g_loss: 0.7289411425590515
Epoch: 6 -- Step: 10970 -- d_loss: 1.0967581272125244 -- g_loss: 0.7390775680541992
Epoch: 6 -- Step: 10980 -- d_loss: 1.1378180980682373 -- g_loss: 0.5885270833969116
Epoch: 6 -- Step: 10990 -- d_loss: 1.44753098487854 -- g_loss: 0.3896762430667877
Epoch: 6 -- Step: 11000 -- d_loss: 1.0031251907348633 -- g_loss: 1.4649103879928589
Epoch: 6 -- Step: 11010 -- d_loss: 1.250216007232666 -- g_loss: 0.6818251013755798
Epoch: 6 -- Step: 11020 -- d_loss: 1.1609766483306885 -- g_loss: 0.8650136590003967
Epoch: 6 -- Step: 11030 -- d_loss: 0.8658730983734131 -- g_loss: 0.9296154975891113
Epoch: 6 -- Step: 11040 -- d_loss: 1.236820936203003 -- g_loss: 0.79111647605896
Epoch: 6 -- Step: 11050 -- d_loss: 1.3295186758041382 -- g_loss: 0.4402153491973877
Epoch: 6 -- Step: 11060 -- d_loss: 1.1830716133117676 -- g_loss: 1.3303173780441284
Epoch: 6 -- Step: 11070 -- d_loss: 1.2542099952697754 -- g_loss: 0.544526994228363
Epoch: 7 -- Step: 11080 -- d_loss: 0.9744337797164917 -- g_loss: 0.9232884049415588
Epoch: 7 -- Step: 11090 -- d_loss: 0.8487421274185181 -- g_loss: 1.3870162963867188
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Epoch: 7 -- Step: 11110 -- d_loss: 1.0321251153945923 -- g_loss: 0.7046724557876587
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Epoch: 7 -- Step: 11130 -- d_loss: 1.4318994283676147 -- g_loss: 0.4650779068470001
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Epoch: 7 -- Step: 11170 -- d_loss: 1.159820556640625 -- g_loss: 0.982932448387146
Epoch: 7 -- Step: 11180 -- d_loss: 1.2624914646148682 -- g_loss: 0.9252070784568787
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Epoch: 7 -- Step: 11200 -- d_loss: 1.7593756914138794 -- g_loss: 0.26786959171295166
Epoch: 7 -- Step: 11210 -- d_loss: 1.207503318786621 -- g_loss: 0.6709213256835938
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Epoch: 7 -- Step: 11230 -- d_loss: 1.0714187622070312 -- g_loss: 0.7713931798934937
Epoch: 7 -- Step: 11240 -- d_loss: 1.244100570678711 -- g_loss: 0.6476364135742188
Epoch: 7 -- Step: 11250 -- d_loss: 0.9983870983123779 -- g_loss: 0.8220394849777222
Epoch: 7 -- Step: 11260 -- d_loss: 0.8730528354644775 -- g_loss: 1.2693709135055542
Epoch: 7 -- Step: 11270 -- d_loss: 0.6682069301605225 -- g_loss: 1.463077187538147
Epoch: 7 -- Step: 11280 -- d_loss: 0.9940256476402283 -- g_loss: 0.8354507684707642
Epoch: 7 -- Step: 11290 -- d_loss: 0.9944977760314941 -- g_loss: 0.7875751256942749
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Epoch: 7 -- Step: 11310 -- d_loss: 0.9753280282020569 -- g_loss: 0.9025734663009644
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Epoch: 7 -- Step: 11330 -- d_loss: 1.2116124629974365 -- g_loss: 1.0820744037628174
Epoch: 7 -- Step: 11340 -- d_loss: 0.967471718788147 -- g_loss: 0.7502217888832092
Epoch: 7 -- Step: 11350 -- d_loss: 1.0407581329345703 -- g_loss: 0.7056772112846375
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Epoch: 7 -- Step: 11370 -- d_loss: 1.0694903135299683 -- g_loss: 0.6156832575798035
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Epoch: 7 -- Step: 11390 -- d_loss: 1.3430726528167725 -- g_loss: 1.5192850828170776
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Epoch: 7 -- Step: 11590 -- d_loss: 0.7955375909805298 -- g_loss: 1.235803246498108
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Epoch: 7 -- Step: 11990 -- d_loss: 0.9123775959014893 -- g_loss: 0.827262818813324
Epoch: 7 -- Step: 12000 -- d_loss: 1.052114725112915 -- g_loss: 0.6786938905715942
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Epoch: 7 -- Step: 12200 -- d_loss: 0.8733896017074585 -- g_loss: 1.1398510932922363
Epoch: 7 -- Step: 12210 -- d_loss: 1.331820011138916 -- g_loss: 2.1809120178222656
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Epoch: 7 -- Step: 12230 -- d_loss: 1.2996025085449219 -- g_loss: 0.42632097005844116
Epoch: 7 -- Step: 12240 -- d_loss: 1.1837323904037476 -- g_loss: 0.623096764087677
Epoch: 7 -- Step: 12250 -- d_loss: 1.2569106817245483 -- g_loss: 0.46964526176452637
Epoch: 7 -- Step: 12260 -- d_loss: 1.0598907470703125 -- g_loss: 1.0876237154006958
Epoch: 7 -- Step: 12270 -- d_loss: 1.070013403892517 -- g_loss: 0.6512229442596436
Epoch: 7 -- Step: 12280 -- d_loss: 0.8819301724433899 -- g_loss: 0.9638974666595459
Epoch: 7 -- Step: 12290 -- d_loss: 1.1348261833190918 -- g_loss: 0.6368581652641296
Epoch: 7 -- Step: 12300 -- d_loss: 1.1403800249099731 -- g_loss: 0.5460889339447021
Epoch: 7 -- Step: 12310 -- d_loss: 1.2295979261398315 -- g_loss: 0.6874212026596069
Epoch: 7 -- Step: 12320 -- d_loss: 0.9694486260414124 -- g_loss: 0.9141362309455872
Epoch: 7 -- Step: 12330 -- d_loss: 0.8573565483093262 -- g_loss: 0.9613305926322937
Epoch: 7 -- Step: 12340 -- d_loss: 0.8229280114173889 -- g_loss: 1.1141512393951416
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Epoch: 7 -- Step: 12360 -- d_loss: 0.8411649465560913 -- g_loss: 0.8855013847351074
Epoch: 7 -- Step: 12370 -- d_loss: 0.9276137948036194 -- g_loss: 0.7464712858200073
Epoch: 7 -- Step: 12380 -- d_loss: 1.1134576797485352 -- g_loss: 0.6231322288513184
Epoch: 7 -- Step: 12390 -- d_loss: 1.0208903551101685 -- g_loss: 0.6353693008422852
Epoch: 7 -- Step: 12400 -- d_loss: 1.0962965488433838 -- g_loss: 0.6912500858306885
Epoch: 7 -- Step: 12410 -- d_loss: 1.143533706665039 -- g_loss: 0.5643879175186157
Epoch: 7 -- Step: 12420 -- d_loss: 1.126146674156189 -- g_loss: 0.6254990696907043
Epoch: 7 -- Step: 12430 -- d_loss: 1.0776898860931396 -- g_loss: 0.8272290825843811
Epoch: 7 -- Step: 12440 -- d_loss: 1.136488914489746 -- g_loss: 0.9487131834030151
Epoch: 7 -- Step: 12450 -- d_loss: 0.994693398475647 -- g_loss: 0.7710354924201965
Epoch: 7 -- Step: 12460 -- d_loss: 1.343679666519165 -- g_loss: 1.4697628021240234
Epoch: 7 -- Step: 12470 -- d_loss: 1.376383662223816 -- g_loss: 0.40262675285339355
Epoch: 7 -- Step: 12480 -- d_loss: 1.1270413398742676 -- g_loss: 0.9733539819717407
Epoch: 7 -- Step: 12490 -- d_loss: 1.0395601987838745 -- g_loss: 0.9820035696029663
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Epoch: 7 -- Step: 12530 -- d_loss: 1.1900911331176758 -- g_loss: 0.9925256967544556
Epoch: 7 -- Step: 12540 -- d_loss: 1.145676612854004 -- g_loss: 0.8847962617874146
Epoch: 7 -- Step: 12550 -- d_loss: 1.2016080617904663 -- g_loss: 0.6112751960754395
Epoch: 7 -- Step: 12560 -- d_loss: 0.7877948880195618 -- g_loss: 0.8571693897247314
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Epoch: 7 -- Step: 12590 -- d_loss: 1.5863507986068726 -- g_loss: 0.33721596002578735
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Epoch: 7 -- Step: 12610 -- d_loss: 1.1396149396896362 -- g_loss: 0.5998074412345886
Epoch: 7 -- Step: 12620 -- d_loss: 1.0805811882019043 -- g_loss: 0.7159701585769653
Epoch: 7 -- Step: 12630 -- d_loss: 0.974211573600769 -- g_loss: 0.846624493598938
Epoch: 7 -- Step: 12640 -- d_loss: 0.7902294397354126 -- g_loss: 1.917959213256836
Epoch: 7 -- Step: 12650 -- d_loss: 1.1891896724700928 -- g_loss: 0.6265522837638855
Epoch: 8 -- Step: 12660 -- d_loss: 1.102083683013916 -- g_loss: 0.5777502059936523
Epoch: 8 -- Step: 12670 -- d_loss: 1.27796471118927 -- g_loss: 0.49178922176361084
Epoch: 8 -- Step: 12680 -- d_loss: 1.1512302160263062 -- g_loss: 0.9207201600074768
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Epoch: 8 -- Step: 12710 -- d_loss: 0.9612985253334045 -- g_loss: 1.0252301692962646
Epoch: 8 -- Step: 12720 -- d_loss: 0.8791574835777283 -- g_loss: 1.0395574569702148
Epoch: 8 -- Step: 12730 -- d_loss: 1.034727692604065 -- g_loss: 0.6693248748779297
Epoch: 8 -- Step: 12740 -- d_loss: 1.3616414070129395 -- g_loss: 0.41340863704681396
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Epoch: 8 -- Step: 12760 -- d_loss: 0.6119101047515869 -- g_loss: 1.2706782817840576
Epoch: 8 -- Step: 12770 -- d_loss: 0.9215384125709534 -- g_loss: 0.8880575299263
Epoch: 8 -- Step: 12780 -- d_loss: 1.361535906791687 -- g_loss: 0.4223721921443939
Epoch: 8 -- Step: 12790 -- d_loss: 1.3657028675079346 -- g_loss: 0.3941657543182373
Epoch: 8 -- Step: 12800 -- d_loss: 1.1809824705123901 -- g_loss: 0.6180216073989868
Epoch: 8 -- Step: 12810 -- d_loss: 0.8847187757492065 -- g_loss: 1.0728013515472412
Epoch: 8 -- Step: 12820 -- d_loss: 0.916312038898468 -- g_loss: 0.972099781036377
Epoch: 8 -- Step: 12830 -- d_loss: 0.8757873773574829 -- g_loss: 1.651344656944275
Epoch: 8 -- Step: 12840 -- d_loss: 1.5248231887817383 -- g_loss: 0.3414245843887329
Epoch: 8 -- Step: 12850 -- d_loss: 1.4036471843719482 -- g_loss: 0.41217511892318726
Epoch: 8 -- Step: 12860 -- d_loss: 1.0934906005859375 -- g_loss: 1.0806725025177002
Epoch: 8 -- Step: 12870 -- d_loss: 0.9989306926727295 -- g_loss: 0.9148115515708923
Epoch: 8 -- Step: 12880 -- d_loss: 1.683258056640625 -- g_loss: 0.35995328426361084
Epoch: 8 -- Step: 12890 -- d_loss: 0.9905226230621338 -- g_loss: 0.8720383644104004
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Epoch: 8 -- Step: 12910 -- d_loss: 1.2490284442901611 -- g_loss: 0.636626124382019
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Epoch: 8 -- Step: 12950 -- d_loss: 0.618116021156311 -- g_loss: 1.6207640171051025
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Epoch: 8 -- Step: 12970 -- d_loss: 1.1031608581542969 -- g_loss: 0.5653397440910339
Epoch: 8 -- Step: 12980 -- d_loss: 0.9528390169143677 -- g_loss: 0.8414144515991211
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Epoch: 8 -- Step: 13030 -- d_loss: 1.0921863317489624 -- g_loss: 0.6663200855255127
Epoch: 8 -- Step: 13040 -- d_loss: 1.091064453125 -- g_loss: 0.6591607332229614
Epoch: 8 -- Step: 13050 -- d_loss: 1.143558382987976 -- g_loss: 1.261460781097412
Epoch: 8 -- Step: 13060 -- d_loss: 1.1131993532180786 -- g_loss: 0.8023656010627747
Epoch: 8 -- Step: 13070 -- d_loss: 1.2336699962615967 -- g_loss: 0.5257002711296082
Epoch: 8 -- Step: 13080 -- d_loss: 0.940981388092041 -- g_loss: 0.729280948638916
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Epoch: 8 -- Step: 13230 -- d_loss: 1.1547024250030518 -- g_loss: 0.8713415265083313
Epoch: 8 -- Step: 13240 -- d_loss: 1.501924753189087 -- g_loss: 0.37471187114715576
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Epoch: 8 -- Step: 13290 -- d_loss: 1.951766014099121 -- g_loss: 0.24161961674690247
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Epoch: 8 -- Step: 13310 -- d_loss: 0.842781662940979 -- g_loss: 1.0309008359909058
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Epoch: 8 -- Step: 13330 -- d_loss: 0.857024073600769 -- g_loss: 0.8878856301307678
Epoch: 8 -- Step: 13340 -- d_loss: 1.6086732149124146 -- g_loss: 1.9538143873214722
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Epoch: 8 -- Step: 13360 -- d_loss: 1.167704463005066 -- g_loss: 0.6203535199165344
Epoch: 8 -- Step: 13370 -- d_loss: 1.0087071657180786 -- g_loss: 0.8999236822128296
Epoch: 8 -- Step: 13380 -- d_loss: 0.954098641872406 -- g_loss: 0.95665043592453
Epoch: 8 -- Step: 13390 -- d_loss: 1.0212225914001465 -- g_loss: 0.7734150290489197
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Epoch: 8 -- Step: 13430 -- d_loss: 1.0051076412200928 -- g_loss: 0.7786106467247009
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Epoch: 8 -- Step: 13480 -- d_loss: 1.5383955240249634 -- g_loss: 0.42362716794013977
Epoch: 8 -- Step: 13490 -- d_loss: 1.5489474534988403 -- g_loss: 0.3775220513343811
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Epoch: 8 -- Step: 13530 -- d_loss: 0.7745317816734314 -- g_loss: 1.083754539489746
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Epoch: 8 -- Step: 13690 -- d_loss: 0.9858261346817017 -- g_loss: 0.7737518548965454
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Epoch: 8 -- Step: 13730 -- d_loss: 1.0075894594192505 -- g_loss: 0.6863888502120972
Epoch: 8 -- Step: 13740 -- d_loss: 0.984955370426178 -- g_loss: 1.1199426651000977
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Epoch: 9 -- Step: 14240 -- d_loss: 0.819584846496582 -- g_loss: 1.162147045135498
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Epoch: 9 -- Step: 14440 -- d_loss: 0.929086446762085 -- g_loss: 0.7707109451293945
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Epoch: 10 -- Step: 16560 -- d_loss: 1.2517032623291016 -- g_loss: 0.5514727234840393
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Epoch: 10 -- Step: 16580 -- d_loss: 0.6353418231010437 -- g_loss: 1.4042720794677734
Epoch: 10 -- Step: 16590 -- d_loss: 0.8517782688140869 -- g_loss: 0.888103723526001
Epoch: 10 -- Step: 16600 -- d_loss: 1.5606133937835693 -- g_loss: 0.3528580963611603
Epoch: 10 -- Step: 16610 -- d_loss: 0.9637395143508911 -- g_loss: 0.8234476447105408
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Epoch: 10 -- Step: 16640 -- d_loss: 1.3324692249298096 -- g_loss: 0.42229074239730835
Epoch: 10 -- Step: 16650 -- d_loss: 0.7672669887542725 -- g_loss: 1.0479408502578735
Epoch: 10 -- Step: 16660 -- d_loss: 1.1545395851135254 -- g_loss: 0.5791305899620056
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Epoch: 10 -- Step: 16690 -- d_loss: 1.2370140552520752 -- g_loss: 0.7061368227005005
Epoch: 10 -- Step: 16700 -- d_loss: 0.7772005796432495 -- g_loss: 1.2458548545837402
Epoch: 10 -- Step: 16710 -- d_loss: 0.4369719624519348 -- g_loss: 2.2524917125701904
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Epoch: 10 -- Step: 16740 -- d_loss: 1.1951730251312256 -- g_loss: 0.5241636037826538
Epoch: 10 -- Step: 16750 -- d_loss: 1.0764235258102417 -- g_loss: 1.6636296510696411
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Epoch: 10 -- Step: 17090 -- d_loss: 1.357397437095642 -- g_loss: 0.4437989592552185
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Epoch: 11 -- Step: 17410 -- d_loss: 1.1282958984375 -- g_loss: 0.6285308599472046
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Epoch: 11 -- Step: 17470 -- d_loss: 3.0197958946228027 -- g_loss: 2.157222032546997
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Epoch: 11 -- Step: 17490 -- d_loss: 1.2936162948608398 -- g_loss: 0.5270377993583679
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Epoch: 11 -- Step: 17540 -- d_loss: 0.7704065442085266 -- g_loss: 1.0880805253982544
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Epoch: 11 -- Step: 17560 -- d_loss: 1.4901459217071533 -- g_loss: 0.4986686110496521
Epoch: 11 -- Step: 17570 -- d_loss: 1.2829585075378418 -- g_loss: 0.5061498284339905
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Epoch: 11 -- Step: 18630 -- d_loss: 0.9493484497070312 -- g_loss: 0.9217523336410522
Epoch: 11 -- Step: 18640 -- d_loss: 0.8302546739578247 -- g_loss: 1.0944528579711914
Epoch: 11 -- Step: 18650 -- d_loss: 1.2573070526123047 -- g_loss: 0.5049769878387451
Epoch: 11 -- Step: 18660 -- d_loss: 1.2667306661605835 -- g_loss: 0.503771185874939
Epoch: 11 -- Step: 18670 -- d_loss: 1.02207350730896 -- g_loss: 1.0827569961547852
Epoch: 11 -- Step: 18680 -- d_loss: 0.7149192094802856 -- g_loss: 1.3488080501556396
Epoch: 11 -- Step: 18690 -- d_loss: 0.6995642185211182 -- g_loss: 1.153823971748352
Epoch: 11 -- Step: 18700 -- d_loss: 0.8796994090080261 -- g_loss: 1.0764697790145874
Epoch: 11 -- Step: 18710 -- d_loss: 1.2963494062423706 -- g_loss: 0.49487876892089844
Epoch: 11 -- Step: 18720 -- d_loss: 0.9308723211288452 -- g_loss: 0.9965156316757202
Epoch: 11 -- Step: 18730 -- d_loss: 0.7538131475448608 -- g_loss: 1.2729660272598267
Epoch: 11 -- Step: 18740 -- d_loss: 1.0739655494689941 -- g_loss: 0.6119025349617004
Epoch: 11 -- Step: 18750 -- d_loss: 1.4195162057876587 -- g_loss: 0.3903609812259674
Epoch: 11 -- Step: 18760 -- d_loss: 0.9069509506225586 -- g_loss: 1.2316875457763672
Epoch: 11 -- Step: 18770 -- d_loss: 1.252455472946167 -- g_loss: 0.49340829253196716
Epoch: 11 -- Step: 18780 -- d_loss: 1.1233282089233398 -- g_loss: 0.5944230556488037
Epoch: 11 -- Step: 18790 -- d_loss: 1.3727235794067383 -- g_loss: 0.4231291115283966
Epoch: 11 -- Step: 18800 -- d_loss: 1.2077457904815674 -- g_loss: 0.6970786452293396
Epoch: 11 -- Step: 18810 -- d_loss: 1.4509060382843018 -- g_loss: 0.39645251631736755
Epoch: 11 -- Step: 18820 -- d_loss: 1.0456374883651733 -- g_loss: 0.6242221593856812
Epoch: 11 -- Step: 18830 -- d_loss: 1.1708569526672363 -- g_loss: 0.7083358764648438
Epoch: 11 -- Step: 18840 -- d_loss: 1.1229400634765625 -- g_loss: 0.9108433127403259
Epoch: 11 -- Step: 18850 -- d_loss: 1.3946013450622559 -- g_loss: 1.2493823766708374
Epoch: 11 -- Step: 18860 -- d_loss: 0.9588843584060669 -- g_loss: 0.714583158493042
Epoch: 11 -- Step: 18870 -- d_loss: 1.2714357376098633 -- g_loss: 0.5304762721061707
Epoch: 11 -- Step: 18880 -- d_loss: 1.508952260017395 -- g_loss: 0.33883798122406006
Epoch: 11 -- Step: 18890 -- d_loss: 1.0933287143707275 -- g_loss: 0.7672580480575562
Epoch: 11 -- Step: 18900 -- d_loss: 1.1039860248565674 -- g_loss: 0.7973601818084717
Epoch: 11 -- Step: 18910 -- d_loss: 1.1416707038879395 -- g_loss: 0.5994952321052551
Epoch: 11 -- Step: 18920 -- d_loss: 1.4215078353881836 -- g_loss: 0.4974589943885803
Epoch: 11 -- Step: 18930 -- d_loss: 0.9300053119659424 -- g_loss: 1.3268548250198364
Epoch: 11 -- Step: 18940 -- d_loss: 0.775977611541748 -- g_loss: 0.9475148916244507
Epoch: 11 -- Step: 18950 -- d_loss: 0.9635107517242432 -- g_loss: 0.8733474016189575
Epoch: 11 -- Step: 18960 -- d_loss: 1.1257367134094238 -- g_loss: 0.8178797960281372
Epoch: 11 -- Step: 18970 -- d_loss: 1.1555193662643433 -- g_loss: 1.6549004316329956
Epoch: 11 -- Step: 18980 -- d_loss: 1.1135753393173218 -- g_loss: 1.2434580326080322
Epoch: 12 -- Step: 18990 -- d_loss: 1.0924501419067383 -- g_loss: 0.6800224781036377
Epoch: 12 -- Step: 19000 -- d_loss: 0.9605216383934021 -- g_loss: 0.9837055206298828
Epoch: 12 -- Step: 19010 -- d_loss: 1.4719569683074951 -- g_loss: 0.4365508258342743
Epoch: 12 -- Step: 19020 -- d_loss: 1.2513550519943237 -- g_loss: 0.5323065519332886
Epoch: 12 -- Step: 19030 -- d_loss: 0.9888792037963867 -- g_loss: 0.694690465927124
Epoch: 12 -- Step: 19040 -- d_loss: 1.1680716276168823 -- g_loss: 0.6302215456962585
Epoch: 12 -- Step: 19050 -- d_loss: 0.8791837096214294 -- g_loss: 0.9861879348754883
Epoch: 12 -- Step: 19060 -- d_loss: 1.1872143745422363 -- g_loss: 0.5325376391410828
Epoch: 12 -- Step: 19070 -- d_loss: 1.3326503038406372 -- g_loss: 0.5503972768783569
Epoch: 12 -- Step: 19080 -- d_loss: 1.0980513095855713 -- g_loss: 0.9403333067893982
Epoch: 12 -- Step: 19090 -- d_loss: 1.2626457214355469 -- g_loss: 0.56915283203125
Epoch: 12 -- Step: 19100 -- d_loss: 1.052067518234253 -- g_loss: 0.7179424166679382
Epoch: 12 -- Step: 19110 -- d_loss: 1.2393364906311035 -- g_loss: 0.594485878944397
Epoch: 12 -- Step: 19120 -- d_loss: 1.076331377029419 -- g_loss: 1.1851521730422974
Epoch: 12 -- Step: 19130 -- d_loss: 1.2827417850494385 -- g_loss: 0.6249014139175415
Epoch: 12 -- Step: 19140 -- d_loss: 1.09578275680542 -- g_loss: 0.7640044689178467
Epoch: 12 -- Step: 19150 -- d_loss: 1.2882733345031738 -- g_loss: 1.8189890384674072
Epoch: 12 -- Step: 19160 -- d_loss: 0.8718900084495544 -- g_loss: 0.9503923058509827
Epoch: 12 -- Step: 19170 -- d_loss: 0.7670528888702393 -- g_loss: 1.098707675933838
Epoch: 12 -- Step: 19180 -- d_loss: 1.1080151796340942 -- g_loss: 0.6278394460678101
Epoch: 12 -- Step: 19190 -- d_loss: 0.9574662446975708 -- g_loss: 0.8771268725395203
Epoch: 12 -- Step: 19200 -- d_loss: 0.9673634767532349 -- g_loss: 0.7720410227775574
Epoch: 12 -- Step: 19210 -- d_loss: 0.985927939414978 -- g_loss: 1.2913310527801514
Epoch: 12 -- Step: 19220 -- d_loss: 1.1068220138549805 -- g_loss: 0.7102451920509338
Epoch: 12 -- Step: 19230 -- d_loss: 1.0952672958374023 -- g_loss: 0.8508473038673401
Epoch: 12 -- Step: 19240 -- d_loss: 1.0210459232330322 -- g_loss: 1.5166834592819214
Epoch: 12 -- Step: 19250 -- d_loss: 1.1719708442687988 -- g_loss: 0.5498238801956177
Epoch: 12 -- Step: 19260 -- d_loss: 1.1285096406936646 -- g_loss: 0.6857969760894775
Epoch: 12 -- Step: 19270 -- d_loss: 1.3988171815872192 -- g_loss: 0.4963056743144989
Epoch: 12 -- Step: 19280 -- d_loss: 1.3104631900787354 -- g_loss: 0.5826963186264038
Epoch: 12 -- Step: 19290 -- d_loss: 1.3042441606521606 -- g_loss: 0.5846654176712036
Epoch: 12 -- Step: 19300 -- d_loss: 1.140815019607544 -- g_loss: 1.2234385013580322
Epoch: 12 -- Step: 19310 -- d_loss: 0.766440212726593 -- g_loss: 1.155199408531189
Epoch: 12 -- Step: 19320 -- d_loss: 1.0123984813690186 -- g_loss: 0.7151648998260498
Epoch: 12 -- Step: 19330 -- d_loss: 1.1888445615768433 -- g_loss: 0.5854496955871582
Epoch: 12 -- Step: 19340 -- d_loss: 1.2098355293273926 -- g_loss: 0.6619240045547485
Epoch: 12 -- Step: 19350 -- d_loss: 0.9670757055282593 -- g_loss: 1.1979395151138306
Epoch: 12 -- Step: 19360 -- d_loss: 0.6295347213745117 -- g_loss: 1.4219872951507568
Epoch: 12 -- Step: 19370 -- d_loss: 1.1223466396331787 -- g_loss: 0.5467724800109863
Epoch: 12 -- Step: 19380 -- d_loss: 0.6719810366630554 -- g_loss: 1.075749397277832
Epoch: 12 -- Step: 19390 -- d_loss: 1.2777292728424072 -- g_loss: 0.4956502914428711
Epoch: 12 -- Step: 19400 -- d_loss: 1.1566845178604126 -- g_loss: 0.8335860371589661
Epoch: 12 -- Step: 19410 -- d_loss: 1.0898879766464233 -- g_loss: 0.6995816230773926
Epoch: 12 -- Step: 19420 -- d_loss: 0.7585432529449463 -- g_loss: 1.0755767822265625
Epoch: 12 -- Step: 19430 -- d_loss: 1.2134056091308594 -- g_loss: 0.5211421847343445
Epoch: 12 -- Step: 19440 -- d_loss: 1.4068214893341064 -- g_loss: 0.9524615406990051
Epoch: 12 -- Step: 19450 -- d_loss: 0.855114221572876 -- g_loss: 1.097182035446167
Epoch: 12 -- Step: 19460 -- d_loss: 1.042945384979248 -- g_loss: 0.6073460578918457
Epoch: 12 -- Step: 19470 -- d_loss: 1.0770832300186157 -- g_loss: 1.3304498195648193
Epoch: 12 -- Step: 19480 -- d_loss: 1.0187711715698242 -- g_loss: 0.675287127494812
Epoch: 12 -- Step: 19490 -- d_loss: 1.209165334701538 -- g_loss: 0.6464220285415649
Epoch: 12 -- Step: 19500 -- d_loss: 1.106520414352417 -- g_loss: 0.8374960422515869
Epoch: 12 -- Step: 19510 -- d_loss: 1.030729055404663 -- g_loss: 1.2134315967559814
Epoch: 12 -- Step: 19520 -- d_loss: 1.08700430393219 -- g_loss: 1.0883996486663818
Epoch: 12 -- Step: 19530 -- d_loss: 0.9153639078140259 -- g_loss: 0.7323526740074158
Epoch: 12 -- Step: 19540 -- d_loss: 1.0965547561645508 -- g_loss: 0.8469128012657166
Epoch: 12 -- Step: 19550 -- d_loss: 1.0204243659973145 -- g_loss: 0.6142439246177673
Epoch: 12 -- Step: 19560 -- d_loss: 1.0658323764801025 -- g_loss: 0.6990053057670593
Epoch: 12 -- Step: 19570 -- d_loss: 0.861825168132782 -- g_loss: 0.9893829822540283
Epoch: 12 -- Step: 19580 -- d_loss: 0.8823946714401245 -- g_loss: 0.8197308778762817
Epoch: 12 -- Step: 19590 -- d_loss: 0.9126911759376526 -- g_loss: 0.855139970779419
Epoch: 12 -- Step: 19600 -- d_loss: 1.1356868743896484 -- g_loss: 0.6913754940032959
Epoch: 12 -- Step: 19610 -- d_loss: 0.9614613056182861 -- g_loss: 0.8951261043548584
Epoch: 12 -- Step: 19620 -- d_loss: 1.1834776401519775 -- g_loss: 0.5605281591415405
Epoch: 12 -- Step: 19630 -- d_loss: 0.7942339181900024 -- g_loss: 0.9050487279891968
Epoch: 12 -- Step: 19640 -- d_loss: 1.3476310968399048 -- g_loss: 0.4760904610157013
Epoch: 12 -- Step: 19650 -- d_loss: 1.2126878499984741 -- g_loss: 0.5748329758644104
Epoch: 12 -- Step: 19660 -- d_loss: 1.0457239151000977 -- g_loss: 0.6238250732421875
Epoch: 12 -- Step: 19670 -- d_loss: 0.8722617626190186 -- g_loss: 0.8567460775375366
Epoch: 12 -- Step: 19680 -- d_loss: 1.3956899642944336 -- g_loss: 0.4258440136909485
Epoch: 12 -- Step: 19690 -- d_loss: 1.4164044857025146 -- g_loss: 0.385517954826355
Epoch: 12 -- Step: 19700 -- d_loss: 0.8204923868179321 -- g_loss: 1.1200714111328125
Epoch: 12 -- Step: 19710 -- d_loss: 1.2241718769073486 -- g_loss: 1.4467321634292603
Epoch: 12 -- Step: 19720 -- d_loss: 1.1636914014816284 -- g_loss: 0.6906036734580994
Epoch: 12 -- Step: 19730 -- d_loss: 0.8759862184524536 -- g_loss: 1.0952484607696533
Epoch: 12 -- Step: 19740 -- d_loss: 0.9623962044715881 -- g_loss: 0.8714002370834351
Epoch: 12 -- Step: 19750 -- d_loss: 1.1095908880233765 -- g_loss: 0.8386528491973877
Epoch: 12 -- Step: 19760 -- d_loss: 1.1252038478851318 -- g_loss: 0.5882586240768433
Epoch: 12 -- Step: 19770 -- d_loss: 1.1529642343521118 -- g_loss: 0.893997311592102
Epoch: 12 -- Step: 19780 -- d_loss: 1.0301522016525269 -- g_loss: 0.6850587129592896
Epoch: 12 -- Step: 19790 -- d_loss: 1.7914364337921143 -- g_loss: 0.2608281970024109
Epoch: 12 -- Step: 19800 -- d_loss: 1.0110498666763306 -- g_loss: 0.6953454613685608
Epoch: 12 -- Step: 19810 -- d_loss: 0.9926352500915527 -- g_loss: 1.4246388673782349
Epoch: 12 -- Step: 19820 -- d_loss: 1.090907335281372 -- g_loss: 0.6315052509307861
Epoch: 12 -- Step: 19830 -- d_loss: 1.0428153276443481 -- g_loss: 0.7024847269058228
Epoch: 12 -- Step: 19840 -- d_loss: 1.1290053129196167 -- g_loss: 0.6039705276489258
Epoch: 12 -- Step: 19850 -- d_loss: 1.1172291040420532 -- g_loss: 0.7113810777664185
Epoch: 12 -- Step: 19860 -- d_loss: 1.2335447072982788 -- g_loss: 0.5512961149215698
Epoch: 12 -- Step: 19870 -- d_loss: 1.031771183013916 -- g_loss: 1.634578824043274
Epoch: 12 -- Step: 19880 -- d_loss: 1.1848385334014893 -- g_loss: 0.520596444606781
Epoch: 12 -- Step: 19890 -- d_loss: 0.9931484460830688 -- g_loss: 0.7480841875076294
Epoch: 12 -- Step: 19900 -- d_loss: 0.9994392395019531 -- g_loss: 0.8426039814949036
Epoch: 12 -- Step: 19910 -- d_loss: 1.4105732440948486 -- g_loss: 1.323056697845459
Epoch: 12 -- Step: 19920 -- d_loss: 1.0614051818847656 -- g_loss: 0.7076875567436218
Epoch: 12 -- Step: 19930 -- d_loss: 0.9310916066169739 -- g_loss: 0.8222770690917969
Epoch: 12 -- Step: 19940 -- d_loss: 1.3371485471725464 -- g_loss: 0.7955335378646851
Epoch: 12 -- Step: 19950 -- d_loss: 1.2834115028381348 -- g_loss: 0.5060271620750427
Epoch: 12 -- Step: 19960 -- d_loss: 1.0968016386032104 -- g_loss: 0.6279536485671997
Epoch: 12 -- Step: 19970 -- d_loss: 1.231696605682373 -- g_loss: 0.5710803270339966
Epoch: 12 -- Step: 19980 -- d_loss: 0.9417845010757446 -- g_loss: 0.8396262526512146
Epoch: 12 -- Step: 19990 -- d_loss: 0.7149533629417419 -- g_loss: 1.4149051904678345
Epoch: 12 -- Step: 20000 -- d_loss: 1.18058180809021 -- g_loss: 0.5528945922851562
Epoch: 12 -- Step: 20010 -- d_loss: 0.9714564085006714 -- g_loss: 0.9312304258346558
Epoch: 12 -- Step: 20020 -- d_loss: 1.4997490644454956 -- g_loss: 0.3926222324371338
Epoch: 12 -- Step: 20030 -- d_loss: 1.4684944152832031 -- g_loss: 0.41040176153182983
Epoch: 12 -- Step: 20040 -- d_loss: 0.9799182415008545 -- g_loss: 0.8523507714271545
Epoch: 12 -- Step: 20050 -- d_loss: 1.0130189657211304 -- g_loss: 0.9211360216140747
Epoch: 12 -- Step: 20060 -- d_loss: 1.0903817415237427 -- g_loss: 0.8433190584182739
Epoch: 12 -- Step: 20070 -- d_loss: 1.1483975648880005 -- g_loss: 1.3971655368804932
Epoch: 12 -- Step: 20080 -- d_loss: 1.3887132406234741 -- g_loss: 0.3850342929363251
Epoch: 12 -- Step: 20090 -- d_loss: 1.2470030784606934 -- g_loss: 0.5267333984375
Epoch: 12 -- Step: 20100 -- d_loss: 1.1152704954147339 -- g_loss: 0.6568068265914917
Epoch: 12 -- Step: 20110 -- d_loss: 1.205932855606079 -- g_loss: 0.8410793542861938
Epoch: 12 -- Step: 20120 -- d_loss: 1.1318178176879883 -- g_loss: 0.7884777784347534
Epoch: 12 -- Step: 20130 -- d_loss: 1.0583049058914185 -- g_loss: 0.6819871664047241
Epoch: 12 -- Step: 20140 -- d_loss: 1.193671464920044 -- g_loss: 0.5982804298400879
Epoch: 12 -- Step: 20150 -- d_loss: 1.0757567882537842 -- g_loss: 0.8930763006210327
Epoch: 12 -- Step: 20160 -- d_loss: 0.9000223875045776 -- g_loss: 1.4544503688812256
Epoch: 12 -- Step: 20170 -- d_loss: 1.2690423727035522 -- g_loss: 0.5316692590713501
Epoch: 12 -- Step: 20180 -- d_loss: 1.1535462141036987 -- g_loss: 0.6150920391082764
Epoch: 12 -- Step: 20190 -- d_loss: 0.947577714920044 -- g_loss: 0.8720712661743164
Epoch: 12 -- Step: 20200 -- d_loss: 1.1126779317855835 -- g_loss: 0.9009876251220703
Epoch: 12 -- Step: 20210 -- d_loss: 1.4137111902236938 -- g_loss: 0.4048190712928772
Epoch: 12 -- Step: 20220 -- d_loss: 1.251004934310913 -- g_loss: 0.46357646584510803
Epoch: 12 -- Step: 20230 -- d_loss: 1.1654458045959473 -- g_loss: 0.5737255215644836
Epoch: 12 -- Step: 20240 -- d_loss: 0.9775481224060059 -- g_loss: 0.7448362112045288
Epoch: 12 -- Step: 20250 -- d_loss: 1.1923892498016357 -- g_loss: 1.1902081966400146
Epoch: 12 -- Step: 20260 -- d_loss: 0.9136143922805786 -- g_loss: 1.1204112768173218
Epoch: 12 -- Step: 20270 -- d_loss: 1.1687023639678955 -- g_loss: 0.6931965351104736
Epoch: 12 -- Step: 20280 -- d_loss: 0.9604944586753845 -- g_loss: 0.8585835099220276
Epoch: 12 -- Step: 20290 -- d_loss: 1.1097615957260132 -- g_loss: 0.7206515073776245
Epoch: 12 -- Step: 20300 -- d_loss: 1.014920711517334 -- g_loss: 0.8439787030220032
Epoch: 12 -- Step: 20310 -- d_loss: 1.0125129222869873 -- g_loss: 0.7781174182891846
Epoch: 12 -- Step: 20320 -- d_loss: 1.0454261302947998 -- g_loss: 0.7324532866477966
Epoch: 12 -- Step: 20330 -- d_loss: 1.0546988248825073 -- g_loss: 0.7457481622695923
Epoch: 12 -- Step: 20340 -- d_loss: 1.287384033203125 -- g_loss: 0.520861029624939
Epoch: 12 -- Step: 20350 -- d_loss: 0.8709388375282288 -- g_loss: 1.0224156379699707
Epoch: 12 -- Step: 20360 -- d_loss: 0.8672234416007996 -- g_loss: 1.1922433376312256
Epoch: 12 -- Step: 20370 -- d_loss: 1.281764030456543 -- g_loss: 0.5578885078430176
Epoch: 12 -- Step: 20380 -- d_loss: 1.1956279277801514 -- g_loss: 0.5322486162185669
Epoch: 12 -- Step: 20390 -- d_loss: 1.2648667097091675 -- g_loss: 0.5867926478385925
Epoch: 12 -- Step: 20400 -- d_loss: 1.092695713043213 -- g_loss: 0.7007755637168884
Epoch: 12 -- Step: 20410 -- d_loss: 1.111741065979004 -- g_loss: 0.9938455820083618
Epoch: 12 -- Step: 20420 -- d_loss: 0.97520911693573 -- g_loss: 1.1582531929016113
Epoch: 12 -- Step: 20430 -- d_loss: 1.2538034915924072 -- g_loss: 0.48940885066986084
Epoch: 12 -- Step: 20440 -- d_loss: 1.0867199897766113 -- g_loss: 0.6425738334655762
Epoch: 12 -- Step: 20450 -- d_loss: 1.5215578079223633 -- g_loss: 0.3264688551425934
Epoch: 12 -- Step: 20460 -- d_loss: 0.9849230051040649 -- g_loss: 0.7986977100372314
Epoch: 12 -- Step: 20470 -- d_loss: 0.9810158014297485 -- g_loss: 0.8405129909515381
Epoch: 12 -- Step: 20480 -- d_loss: 0.9676904082298279 -- g_loss: 0.9236190915107727
Epoch: 12 -- Step: 20490 -- d_loss: 0.9205148816108704 -- g_loss: 0.9600104093551636
Epoch: 12 -- Step: 20500 -- d_loss: 1.1496747732162476 -- g_loss: 0.7828084230422974
Epoch: 12 -- Step: 20510 -- d_loss: 1.0321919918060303 -- g_loss: 0.7192984223365784
Epoch: 12 -- Step: 20520 -- d_loss: 1.0976481437683105 -- g_loss: 0.6404796838760376
Epoch: 12 -- Step: 20530 -- d_loss: 0.9347068071365356 -- g_loss: 0.7557522654533386
Epoch: 12 -- Step: 20540 -- d_loss: 1.1795624494552612 -- g_loss: 0.5553503036499023
Epoch: 12 -- Step: 20550 -- d_loss: 1.418971300125122 -- g_loss: 2.462721824645996
Epoch: 12 -- Step: 20560 -- d_loss: 1.172175645828247 -- g_loss: 0.7826447486877441
Epoch: 13 -- Step: 20570 -- d_loss: 1.3450124263763428 -- g_loss: 0.5891152620315552
Epoch: 13 -- Step: 20580 -- d_loss: 1.09476637840271 -- g_loss: 0.6990434527397156
Epoch: 13 -- Step: 20590 -- d_loss: 1.0695183277130127 -- g_loss: 0.9150547981262207
Epoch: 13 -- Step: 20600 -- d_loss: 1.1264581680297852 -- g_loss: 0.610093891620636
Epoch: 13 -- Step: 20610 -- d_loss: 0.9052467942237854 -- g_loss: 0.8151024580001831
Epoch: 13 -- Step: 20620 -- d_loss: 1.3025381565093994 -- g_loss: 0.7033247947692871
Epoch: 13 -- Step: 20630 -- d_loss: 0.9384986162185669 -- g_loss: 0.919851541519165
Epoch: 13 -- Step: 20640 -- d_loss: 0.9230213165283203 -- g_loss: 0.8459117412567139
Epoch: 13 -- Step: 20650 -- d_loss: 1.5145072937011719 -- g_loss: 0.3792020380496979
Epoch: 13 -- Step: 20660 -- d_loss: 0.8012410998344421 -- g_loss: 1.2217576503753662
Epoch: 13 -- Step: 20670 -- d_loss: 1.2085106372833252 -- g_loss: 0.562213659286499
Epoch: 13 -- Step: 20680 -- d_loss: 1.215001106262207 -- g_loss: 0.5356075763702393
Epoch: 13 -- Step: 20690 -- d_loss: 1.334350347518921 -- g_loss: 0.5298475027084351
Epoch: 13 -- Step: 20700 -- d_loss: 0.89906907081604 -- g_loss: 0.8234066963195801
Epoch: 13 -- Step: 20710 -- d_loss: 1.17258620262146 -- g_loss: 1.0610136985778809
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Epoch: 13 -- Step: 21510 -- d_loss: 0.7308766841888428 -- g_loss: 1.1038591861724854
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Epoch: 13 -- Step: 21850 -- d_loss: 1.0962071418762207 -- g_loss: 0.6394126415252686
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Epoch: 13 -- Step: 21870 -- d_loss: 1.066333293914795 -- g_loss: 1.592106819152832
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Epoch: 13 -- Step: 22040 -- d_loss: 0.9111672639846802 -- g_loss: 0.8089606761932373
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Epoch: 13 -- Step: 22110 -- d_loss: 0.9932153820991516 -- g_loss: 0.9030261039733887
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Epoch: 14 -- Step: 22890 -- d_loss: 1.1975356340408325 -- g_loss: 0.613470733165741
Epoch: 14 -- Step: 22900 -- d_loss: 1.1677498817443848 -- g_loss: 0.5728569626808167
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Epoch: 14 -- Step: 22920 -- d_loss: 0.9445185661315918 -- g_loss: 0.8167464733123779
Epoch: 14 -- Step: 22930 -- d_loss: 0.8306543827056885 -- g_loss: 0.9628927707672119
Epoch: 14 -- Step: 22940 -- d_loss: 1.3553388118743896 -- g_loss: 0.4721202552318573
Epoch: 14 -- Step: 22950 -- d_loss: 0.8622260689735413 -- g_loss: 0.9397801160812378
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Epoch: 14 -- Step: 22970 -- d_loss: 1.0779757499694824 -- g_loss: 1.0043606758117676
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Epoch: 14 -- Step: 22990 -- d_loss: 1.5615876913070679 -- g_loss: 0.35144442319869995
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Epoch: 14 -- Step: 23060 -- d_loss: 1.1932151317596436 -- g_loss: 0.6378936767578125
Epoch: 14 -- Step: 23070 -- d_loss: 1.4322810173034668 -- g_loss: 0.5137364864349365
Epoch: 14 -- Step: 23080 -- d_loss: 1.0369937419891357 -- g_loss: 0.7193627953529358
Epoch: 14 -- Step: 23090 -- d_loss: 1.4718815088272095 -- g_loss: 0.4110671877861023
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Epoch: 14 -- Step: 23110 -- d_loss: 0.9792561531066895 -- g_loss: 0.9699592590332031
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Epoch: 14 -- Step: 23130 -- d_loss: 0.988814115524292 -- g_loss: 0.7451855540275574
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Epoch: 14 -- Step: 23150 -- d_loss: 1.3062717914581299 -- g_loss: 0.48673853278160095
Epoch: 14 -- Step: 23160 -- d_loss: 0.8764544129371643 -- g_loss: 0.9079474210739136
Epoch: 14 -- Step: 23170 -- d_loss: 1.1726210117340088 -- g_loss: 0.6153166890144348
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Epoch: 14 -- Step: 23190 -- d_loss: 0.8305791616439819 -- g_loss: 0.9573197364807129
Epoch: 14 -- Step: 23200 -- d_loss: 1.0987976789474487 -- g_loss: 0.7130974531173706
Epoch: 14 -- Step: 23210 -- d_loss: 1.3151509761810303 -- g_loss: 0.42835184931755066
Epoch: 14 -- Step: 23220 -- d_loss: 1.3616094589233398 -- g_loss: 0.499126672744751
Epoch: 14 -- Step: 23230 -- d_loss: 1.2632063627243042 -- g_loss: 0.542911946773529
Epoch: 14 -- Step: 23240 -- d_loss: 1.395207405090332 -- g_loss: 0.39862996339797974
Epoch: 14 -- Step: 23250 -- d_loss: 1.1025173664093018 -- g_loss: 1.2048697471618652
Epoch: 14 -- Step: 23260 -- d_loss: 0.9629380106925964 -- g_loss: 0.8353791832923889
Epoch: 14 -- Step: 23270 -- d_loss: 1.0928678512573242 -- g_loss: 0.6490120887756348
Epoch: 14 -- Step: 23280 -- d_loss: 0.8951185941696167 -- g_loss: 0.9994595646858215
Epoch: 14 -- Step: 23290 -- d_loss: 0.9442300796508789 -- g_loss: 0.77498459815979
Epoch: 14 -- Step: 23300 -- d_loss: 1.1992689371109009 -- g_loss: 0.534907341003418
Epoch: 14 -- Step: 23310 -- d_loss: 1.0491944551467896 -- g_loss: 0.7218364477157593
Epoch: 14 -- Step: 23320 -- d_loss: 1.3009456396102905 -- g_loss: 0.49487775564193726
Epoch: 14 -- Step: 23330 -- d_loss: 0.9648648500442505 -- g_loss: 0.8686031699180603
Epoch: 14 -- Step: 23340 -- d_loss: 1.1409770250320435 -- g_loss: 0.6019839644432068
Epoch: 14 -- Step: 23350 -- d_loss: 1.204289197921753 -- g_loss: 1.3773653507232666
Epoch: 14 -- Step: 23360 -- d_loss: 1.2557787895202637 -- g_loss: 0.4941767752170563
Epoch: 14 -- Step: 23370 -- d_loss: 2.0204148292541504 -- g_loss: 0.19626301527023315
Epoch: 14 -- Step: 23380 -- d_loss: 1.139919400215149 -- g_loss: 0.8956406116485596
Epoch: 14 -- Step: 23390 -- d_loss: 1.1018123626708984 -- g_loss: 0.6614731550216675
Epoch: 14 -- Step: 23400 -- d_loss: 1.0567173957824707 -- g_loss: 0.8269544839859009
Epoch: 14 -- Step: 23410 -- d_loss: 0.815538763999939 -- g_loss: 1.1419031620025635
Epoch: 14 -- Step: 23420 -- d_loss: 0.8709249496459961 -- g_loss: 0.9718772172927856
Epoch: 14 -- Step: 23430 -- d_loss: 0.9200314879417419 -- g_loss: 1.0338118076324463
Epoch: 14 -- Step: 23440 -- d_loss: 0.7570682764053345 -- g_loss: 1.286772608757019
Epoch: 14 -- Step: 23450 -- d_loss: 1.195060133934021 -- g_loss: 0.5840768814086914
Epoch: 14 -- Step: 23460 -- d_loss: 1.1774715185165405 -- g_loss: 0.5739983320236206
Epoch: 14 -- Step: 23470 -- d_loss: 1.5686793327331543 -- g_loss: 0.3421202003955841
Epoch: 14 -- Step: 23480 -- d_loss: 1.1106431484222412 -- g_loss: 0.6179264783859253
Epoch: 14 -- Step: 23490 -- d_loss: 1.347137451171875 -- g_loss: 0.4576694369316101
Epoch: 14 -- Step: 23500 -- d_loss: 0.9661054015159607 -- g_loss: 1.1722748279571533
Epoch: 14 -- Step: 23510 -- d_loss: 0.93170166015625 -- g_loss: 0.9542386531829834
Epoch: 14 -- Step: 23520 -- d_loss: 1.0999888181686401 -- g_loss: 0.6477442979812622
Epoch: 14 -- Step: 23530 -- d_loss: 1.0907602310180664 -- g_loss: 0.7369451522827148
Epoch: 14 -- Step: 23540 -- d_loss: 1.1323010921478271 -- g_loss: 0.7101331949234009
Epoch: 14 -- Step: 23550 -- d_loss: 1.3594709634780884 -- g_loss: 0.4434443712234497
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Epoch: 14 -- Step: 23570 -- d_loss: 1.1376488208770752 -- g_loss: 0.6539151072502136
Epoch: 14 -- Step: 23580 -- d_loss: 0.8414344191551208 -- g_loss: 1.0152018070220947
Epoch: 14 -- Step: 23590 -- d_loss: 1.0835176706314087 -- g_loss: 1.008009433746338
Epoch: 14 -- Step: 23600 -- d_loss: 0.7693985104560852 -- g_loss: 0.99446702003479
Epoch: 14 -- Step: 23610 -- d_loss: 0.8089823722839355 -- g_loss: 1.0860737562179565
Epoch: 14 -- Step: 23620 -- d_loss: 1.2236063480377197 -- g_loss: 0.6427268981933594
Epoch: 14 -- Step: 23630 -- d_loss: 1.0192804336547852 -- g_loss: 0.9630523920059204
Epoch: 14 -- Step: 23640 -- d_loss: 0.8770412802696228 -- g_loss: 1.0425091981887817
Epoch: 14 -- Step: 23650 -- d_loss: 1.080502986907959 -- g_loss: 0.7769888043403625
Epoch: 14 -- Step: 23660 -- d_loss: 1.1362040042877197 -- g_loss: 0.795263409614563
Epoch: 14 -- Step: 23670 -- d_loss: 1.1910152435302734 -- g_loss: 0.5796526670455933
Epoch: 14 -- Step: 23680 -- d_loss: 1.008406400680542 -- g_loss: 0.8270894289016724
Epoch: 14 -- Step: 23690 -- d_loss: 0.825520932674408 -- g_loss: 1.2596091032028198
Epoch: 14 -- Step: 23700 -- d_loss: 0.9757459163665771 -- g_loss: 0.7401700019836426
Epoch: 14 -- Step: 23710 -- d_loss: 1.2086759805679321 -- g_loss: 0.5833483934402466
Epoch: 14 -- Step: 23720 -- d_loss: 1.4112966060638428 -- g_loss: 0.4355434477329254
Epoch: 14 -- Step: 23730 -- d_loss: 0.9739677906036377 -- g_loss: 1.0758947134017944
Epoch: 15 -- Step: 23740 -- d_loss: 1.5890097618103027 -- g_loss: 0.3244030773639679
Epoch: 15 -- Step: 23750 -- d_loss: 1.0537543296813965 -- g_loss: 0.9310712814331055
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Epoch: 15 -- Step: 23770 -- d_loss: 0.757288932800293 -- g_loss: 1.647684097290039
Epoch: 15 -- Step: 23780 -- d_loss: 1.3173189163208008 -- g_loss: 0.7741583585739136
Epoch: 15 -- Step: 23790 -- d_loss: 1.054139494895935 -- g_loss: 0.7175250053405762
Epoch: 15 -- Step: 23800 -- d_loss: 1.374639868736267 -- g_loss: 0.5297493934631348
Epoch: 15 -- Step: 23810 -- d_loss: 0.9289312958717346 -- g_loss: 0.8866093158721924
Epoch: 15 -- Step: 23820 -- d_loss: 1.1230885982513428 -- g_loss: 0.7690287232398987
Epoch: 15 -- Step: 23830 -- d_loss: 1.1312675476074219 -- g_loss: 0.9867594838142395
Epoch: 15 -- Step: 23840 -- d_loss: 1.045823097229004 -- g_loss: 0.9248620271682739
Epoch: 15 -- Step: 23850 -- d_loss: 0.9194333553314209 -- g_loss: 1.0671184062957764
Epoch: 15 -- Step: 23860 -- d_loss: 0.8052334785461426 -- g_loss: 1.003551959991455
Epoch: 15 -- Step: 23870 -- d_loss: 1.2835264205932617 -- g_loss: 1.9505250453948975
Epoch: 15 -- Step: 23880 -- d_loss: 1.0876357555389404 -- g_loss: 0.6902509927749634
Epoch: 15 -- Step: 23890 -- d_loss: 0.7137979865074158 -- g_loss: 1.3375630378723145
Epoch: 15 -- Step: 23900 -- d_loss: 1.032480001449585 -- g_loss: 1.1210482120513916
Epoch: 15 -- Step: 23910 -- d_loss: 0.8101462125778198 -- g_loss: 1.0299408435821533
Epoch: 15 -- Step: 23920 -- d_loss: 0.5324232578277588 -- g_loss: 1.633208990097046
Epoch: 15 -- Step: 23930 -- d_loss: 1.0261039733886719 -- g_loss: 0.6785948872566223
Epoch: 15 -- Step: 23940 -- d_loss: 1.1633551120758057 -- g_loss: 0.7134714722633362
Epoch: 15 -- Step: 23950 -- d_loss: 1.3863155841827393 -- g_loss: 0.45465466380119324
Epoch: 15 -- Step: 23960 -- d_loss: 0.9419220685958862 -- g_loss: 0.7947129011154175
Epoch: 15 -- Step: 23970 -- d_loss: 2.33543062210083 -- g_loss: 0.15089182555675507
Epoch: 15 -- Step: 23980 -- d_loss: 0.8982231020927429 -- g_loss: 0.9104741811752319
Epoch: 15 -- Step: 23990 -- d_loss: 1.8707597255706787 -- g_loss: 0.2800764739513397
Epoch: 15 -- Step: 24000 -- d_loss: 1.847388744354248 -- g_loss: 0.24694402515888214
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Epoch: 15 -- Step: 24030 -- d_loss: 1.3128712177276611 -- g_loss: 0.46439439058303833
Epoch: 15 -- Step: 24040 -- d_loss: 1.0447368621826172 -- g_loss: 0.8072083592414856
Epoch: 15 -- Step: 24050 -- d_loss: 0.9914768934249878 -- g_loss: 0.827174961566925
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Epoch: 15 -- Step: 24070 -- d_loss: 1.005927562713623 -- g_loss: 0.9885238409042358
Epoch: 15 -- Step: 24080 -- d_loss: 0.8510338068008423 -- g_loss: 0.8194538354873657
Epoch: 15 -- Step: 24090 -- d_loss: 0.8888642191886902 -- g_loss: 0.8485872745513916
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Epoch: 15 -- Step: 24150 -- d_loss: 1.0432424545288086 -- g_loss: 0.6606248617172241
Epoch: 15 -- Step: 24160 -- d_loss: 1.2672474384307861 -- g_loss: 0.8295559883117676
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Epoch: 15 -- Step: 24920 -- d_loss: 0.8550031781196594 -- g_loss: 0.8140701651573181
Epoch: 15 -- Step: 24930 -- d_loss: 0.9048609137535095 -- g_loss: 1.328948736190796
Epoch: 15 -- Step: 24940 -- d_loss: 0.9796388149261475 -- g_loss: 0.785921573638916
Epoch: 15 -- Step: 24950 -- d_loss: 1.038435697555542 -- g_loss: 0.6812708377838135
Epoch: 15 -- Step: 24960 -- d_loss: 1.0239582061767578 -- g_loss: 0.7060511112213135
Epoch: 15 -- Step: 24970 -- d_loss: 0.9053743481636047 -- g_loss: 0.9439504146575928
Epoch: 15 -- Step: 24980 -- d_loss: 0.8128654360771179 -- g_loss: 1.1015856266021729
Epoch: 15 -- Step: 24990 -- d_loss: 0.5115372538566589 -- g_loss: 1.8401585817337036
Epoch: 15 -- Step: 25000 -- d_loss: 0.9921500086784363 -- g_loss: 0.8285824656486511
Epoch: 15 -- Step: 25010 -- d_loss: 0.8461467027664185 -- g_loss: 0.9850324988365173
Epoch: 15 -- Step: 25020 -- d_loss: 1.4614992141723633 -- g_loss: 0.3613041043281555
Epoch: 15 -- Step: 25030 -- d_loss: 1.2032142877578735 -- g_loss: 1.3920702934265137
Epoch: 15 -- Step: 25040 -- d_loss: 0.9613320827484131 -- g_loss: 0.7161645889282227
Epoch: 15 -- Step: 25050 -- d_loss: 0.967689573764801 -- g_loss: 0.7386314868927002
Epoch: 15 -- Step: 25060 -- d_loss: 1.1411621570587158 -- g_loss: 0.5814177989959717
Epoch: 15 -- Step: 25070 -- d_loss: 0.9616973400115967 -- g_loss: 0.6591612100601196
Epoch: 15 -- Step: 25080 -- d_loss: 0.8126629590988159 -- g_loss: 1.0255327224731445
Epoch: 15 -- Step: 25090 -- d_loss: 1.2445869445800781 -- g_loss: 0.5137262940406799
Epoch: 15 -- Step: 25100 -- d_loss: 0.91463702917099 -- g_loss: 0.827133297920227
Epoch: 15 -- Step: 25110 -- d_loss: 1.0847264528274536 -- g_loss: 0.7816528677940369
Epoch: 15 -- Step: 25120 -- d_loss: 0.961135745048523 -- g_loss: 0.7648869752883911
Epoch: 15 -- Step: 25130 -- d_loss: 1.004514455795288 -- g_loss: 0.736642599105835
Epoch: 15 -- Step: 25140 -- d_loss: 1.1268706321716309 -- g_loss: 1.7197867631912231
Epoch: 15 -- Step: 25150 -- d_loss: 1.2187483310699463 -- g_loss: 0.591936469078064
Epoch: 15 -- Step: 25160 -- d_loss: 1.3533998727798462 -- g_loss: 0.4397781491279602
Epoch: 15 -- Step: 25170 -- d_loss: 1.0381625890731812 -- g_loss: 0.8426415324211121
Epoch: 15 -- Step: 25180 -- d_loss: 1.251457929611206 -- g_loss: 0.5287143588066101
Epoch: 15 -- Step: 25190 -- d_loss: 1.397373914718628 -- g_loss: 0.4447740912437439
Epoch: 15 -- Step: 25200 -- d_loss: 0.988656759262085 -- g_loss: 0.6949239373207092
Epoch: 15 -- Step: 25210 -- d_loss: 1.0469410419464111 -- g_loss: 0.7554051876068115
Epoch: 15 -- Step: 25220 -- d_loss: 1.0154094696044922 -- g_loss: 0.8980920314788818
Epoch: 15 -- Step: 25230 -- d_loss: 1.0401668548583984 -- g_loss: 0.6709136962890625
Epoch: 15 -- Step: 25240 -- d_loss: 1.469015121459961 -- g_loss: 0.3582119345664978
Epoch: 15 -- Step: 25250 -- d_loss: 1.1706125736236572 -- g_loss: 0.5736014246940613
Epoch: 15 -- Step: 25260 -- d_loss: 1.0628026723861694 -- g_loss: 0.7547166347503662
Epoch: 15 -- Step: 25270 -- d_loss: 1.462671160697937 -- g_loss: 0.35808929800987244
Epoch: 15 -- Step: 25280 -- d_loss: 0.9114338159561157 -- g_loss: 0.9024322032928467
Epoch: 15 -- Step: 25290 -- d_loss: 1.2231436967849731 -- g_loss: 0.48342761397361755
Epoch: 15 -- Step: 25300 -- d_loss: 1.1994837522506714 -- g_loss: 0.5249095559120178
Epoch: 15 -- Step: 25310 -- d_loss: 1.199954867362976 -- g_loss: 0.5013607740402222
Epoch: 16 -- Step: 25320 -- d_loss: 0.9428231716156006 -- g_loss: 0.737453043460846
Epoch: 16 -- Step: 25330 -- d_loss: 0.9785215258598328 -- g_loss: 0.7336521148681641
Epoch: 16 -- Step: 25340 -- d_loss: 0.8454701900482178 -- g_loss: 1.3573256731033325
Epoch: 16 -- Step: 25350 -- d_loss: 0.7501773834228516 -- g_loss: 1.4323147535324097
Epoch: 16 -- Step: 25360 -- d_loss: 0.7566165924072266 -- g_loss: 1.0586662292480469
Epoch: 16 -- Step: 25370 -- d_loss: 0.9317415356636047 -- g_loss: 0.7944796085357666
Epoch: 16 -- Step: 25380 -- d_loss: 1.4424967765808105 -- g_loss: 0.4113575220108032
Epoch: 16 -- Step: 25390 -- d_loss: 0.678371787071228 -- g_loss: 1.1035014390945435
Epoch: 16 -- Step: 25400 -- d_loss: 1.0764731168746948 -- g_loss: 0.6907122135162354
Epoch: 16 -- Step: 25410 -- d_loss: 1.1271090507507324 -- g_loss: 1.0778374671936035
Epoch: 16 -- Step: 25420 -- d_loss: 1.2057069540023804 -- g_loss: 0.5357669591903687
Epoch: 16 -- Step: 25430 -- d_loss: 1.096509575843811 -- g_loss: 0.659665048122406
Epoch: 16 -- Step: 25440 -- d_loss: 0.8889853954315186 -- g_loss: 0.7922021150588989
Epoch: 16 -- Step: 25450 -- d_loss: 0.9076262712478638 -- g_loss: 0.9864630699157715
Epoch: 16 -- Step: 25460 -- d_loss: 1.050191879272461 -- g_loss: 1.3114590644836426
Epoch: 16 -- Step: 25470 -- d_loss: 1.006084680557251 -- g_loss: 0.7679710984230042
Epoch: 16 -- Step: 25480 -- d_loss: 1.0741503238677979 -- g_loss: 0.8501994609832764
Epoch: 16 -- Step: 25490 -- d_loss: 1.0254353284835815 -- g_loss: 0.7038716077804565
Epoch: 16 -- Step: 25500 -- d_loss: 0.9989144802093506 -- g_loss: 0.8198792934417725
Epoch: 16 -- Step: 25510 -- d_loss: 0.9953700304031372 -- g_loss: 0.9130859375
Epoch: 16 -- Step: 25520 -- d_loss: 1.2153488397598267 -- g_loss: 0.5068455934524536
Epoch: 16 -- Step: 25530 -- d_loss: 1.5487642288208008 -- g_loss: 0.4038196802139282
Epoch: 16 -- Step: 25540 -- d_loss: 1.1473822593688965 -- g_loss: 0.6725240349769592
Epoch: 16 -- Step: 25550 -- d_loss: 0.8549702763557434 -- g_loss: 0.7871512174606323
Epoch: 16 -- Step: 25560 -- d_loss: 1.238434076309204 -- g_loss: 1.0675973892211914
Epoch: 16 -- Step: 25570 -- d_loss: 0.8793355226516724 -- g_loss: 1.1174393892288208
Epoch: 16 -- Step: 25580 -- d_loss: 0.9408241510391235 -- g_loss: 0.8154228925704956
Epoch: 16 -- Step: 25590 -- d_loss: 1.6416140794754028 -- g_loss: 0.32491615414619446
Epoch: 16 -- Step: 25600 -- d_loss: 1.2732799053192139 -- g_loss: 0.48371294140815735
Epoch: 16 -- Step: 25610 -- d_loss: 1.0207988023757935 -- g_loss: 1.0715394020080566
Epoch: 16 -- Step: 25620 -- d_loss: 1.0904300212860107 -- g_loss: 0.6611238718032837
Epoch: 16 -- Step: 25630 -- d_loss: 0.9124812483787537 -- g_loss: 1.0828907489776611
Epoch: 16 -- Step: 25640 -- d_loss: 0.7994741797447205 -- g_loss: 0.9076188802719116
Epoch: 16 -- Step: 25650 -- d_loss: 1.6319395303726196 -- g_loss: 0.29608768224716187
Epoch: 16 -- Step: 25660 -- d_loss: 0.9371896982192993 -- g_loss: 0.9312670230865479
Epoch: 16 -- Step: 25670 -- d_loss: 1.3370912075042725 -- g_loss: 0.4485197365283966
Epoch: 16 -- Step: 25680 -- d_loss: 1.323134422302246 -- g_loss: 0.4768795669078827
Epoch: 16 -- Step: 25690 -- d_loss: 1.2439732551574707 -- g_loss: 0.546252965927124
Epoch: 16 -- Step: 25700 -- d_loss: 0.5705269575119019 -- g_loss: 1.5959539413452148
Epoch: 16 -- Step: 25710 -- d_loss: 1.0081663131713867 -- g_loss: 0.6877402663230896
Epoch: 16 -- Step: 25720 -- d_loss: 1.1165392398834229 -- g_loss: 0.8410760760307312
Epoch: 16 -- Step: 25730 -- d_loss: 1.1685055494308472 -- g_loss: 0.563001275062561
Epoch: 16 -- Step: 25740 -- d_loss: 1.1499881744384766 -- g_loss: 0.5582711696624756
Epoch: 16 -- Step: 25750 -- d_loss: 0.762697696685791 -- g_loss: 0.9474097490310669
Epoch: 16 -- Step: 25760 -- d_loss: 1.2680513858795166 -- g_loss: 0.5490131378173828
Epoch: 16 -- Step: 25770 -- d_loss: 1.0619616508483887 -- g_loss: 0.6158775091171265
Epoch: 16 -- Step: 25780 -- d_loss: 0.7197357416152954 -- g_loss: 1.052496314048767
Epoch: 16 -- Step: 25790 -- d_loss: 0.8612603545188904 -- g_loss: 0.9952468872070312
Epoch: 16 -- Step: 25800 -- d_loss: 0.8953145146369934 -- g_loss: 0.9137580394744873
Epoch: 16 -- Step: 25810 -- d_loss: 0.9879676103591919 -- g_loss: 0.8987535238265991
Epoch: 16 -- Step: 25820 -- d_loss: 1.070435643196106 -- g_loss: 0.6988332867622375
Epoch: 16 -- Step: 25830 -- d_loss: 1.178581714630127 -- g_loss: 1.4396336078643799
Epoch: 16 -- Step: 25840 -- d_loss: 0.9321433305740356 -- g_loss: 0.869911789894104
Epoch: 16 -- Step: 25850 -- d_loss: 1.227950096130371 -- g_loss: 0.6357991695404053
Epoch: 16 -- Step: 25860 -- d_loss: 0.9753696918487549 -- g_loss: 0.7895230054855347
Epoch: 16 -- Step: 25870 -- d_loss: 0.9305659532546997 -- g_loss: 1.1633036136627197
Epoch: 16 -- Step: 25880 -- d_loss: 1.021190881729126 -- g_loss: 0.8347293138504028
Epoch: 16 -- Step: 25890 -- d_loss: 0.8511965870857239 -- g_loss: 0.8075845241546631
Epoch: 16 -- Step: 25900 -- d_loss: 1.0972448587417603 -- g_loss: 0.5970886945724487
Epoch: 16 -- Step: 25910 -- d_loss: 1.0443313121795654 -- g_loss: 0.5635772943496704
Epoch: 16 -- Step: 25920 -- d_loss: 1.0814285278320312 -- g_loss: 0.6763445138931274
Epoch: 16 -- Step: 25930 -- d_loss: 1.122579574584961 -- g_loss: 0.7885481119155884
Epoch: 16 -- Step: 25940 -- d_loss: 0.947869062423706 -- g_loss: 0.8914650082588196
Epoch: 16 -- Step: 25950 -- d_loss: 0.7702053785324097 -- g_loss: 1.3963549137115479
Epoch: 16 -- Step: 25960 -- d_loss: 0.8938881158828735 -- g_loss: 1.0988765954971313
Epoch: 16 -- Step: 25970 -- d_loss: 0.6720236539840698 -- g_loss: 1.2293455600738525
Epoch: 16 -- Step: 25980 -- d_loss: 1.35809326171875 -- g_loss: 0.413262277841568
Epoch: 16 -- Step: 25990 -- d_loss: 1.015380620956421 -- g_loss: 1.2036747932434082
Epoch: 16 -- Step: 26000 -- d_loss: 1.1566051244735718 -- g_loss: 1.1556532382965088
Epoch: 16 -- Step: 26010 -- d_loss: 1.2404483556747437 -- g_loss: 0.6974078416824341
Epoch: 16 -- Step: 26020 -- d_loss: 1.1593188047409058 -- g_loss: 0.6471614837646484
Epoch: 16 -- Step: 26030 -- d_loss: 0.7099395990371704 -- g_loss: 1.1092958450317383
Epoch: 16 -- Step: 26040 -- d_loss: 0.8824007511138916 -- g_loss: 1.1235867738723755
Epoch: 16 -- Step: 26050 -- d_loss: 0.8550151586532593 -- g_loss: 0.8736072182655334
Epoch: 16 -- Step: 26060 -- d_loss: 1.2769030332565308 -- g_loss: 0.5806888341903687
Epoch: 16 -- Step: 26070 -- d_loss: 1.0106642246246338 -- g_loss: 0.6690120100975037
Epoch: 16 -- Step: 26080 -- d_loss: 1.1693466901779175 -- g_loss: 0.560382604598999
Epoch: 16 -- Step: 26090 -- d_loss: 1.2632455825805664 -- g_loss: 1.6346250772476196
Epoch: 16 -- Step: 26100 -- d_loss: 1.358408808708191 -- g_loss: 0.44577687978744507
Epoch: 16 -- Step: 26110 -- d_loss: 1.0679327249526978 -- g_loss: 1.2804831266403198
Epoch: 16 -- Step: 26120 -- d_loss: 1.149888038635254 -- g_loss: 0.6252332925796509
Epoch: 16 -- Step: 26130 -- d_loss: 1.3047360181808472 -- g_loss: 0.48689955472946167
Epoch: 16 -- Step: 26140 -- d_loss: 1.0180331468582153 -- g_loss: 0.7149380445480347
Epoch: 16 -- Step: 26150 -- d_loss: 1.67857027053833 -- g_loss: 0.32942700386047363
Epoch: 16 -- Step: 26160 -- d_loss: 0.7595919370651245 -- g_loss: 1.0193203687667847
Epoch: 16 -- Step: 26170 -- d_loss: 0.9564348459243774 -- g_loss: 0.8502704501152039
Epoch: 16 -- Step: 26180 -- d_loss: 1.3074488639831543 -- g_loss: 0.47629791498184204
Epoch: 16 -- Step: 26190 -- d_loss: 1.1961590051651 -- g_loss: 0.5489332675933838
Epoch: 16 -- Step: 26200 -- d_loss: 1.1479483842849731 -- g_loss: 0.5928432941436768
Epoch: 16 -- Step: 26210 -- d_loss: 1.3116629123687744 -- g_loss: 0.5984592437744141
Epoch: 16 -- Step: 26220 -- d_loss: 0.9338294267654419 -- g_loss: 0.9258049130439758
Epoch: 16 -- Step: 26230 -- d_loss: 1.4580906629562378 -- g_loss: 0.4352147579193115
Epoch: 16 -- Step: 26240 -- d_loss: 1.0206494331359863 -- g_loss: 1.0686492919921875
Epoch: 16 -- Step: 26250 -- d_loss: 1.1772874593734741 -- g_loss: 0.5578606128692627
Epoch: 16 -- Step: 26260 -- d_loss: 0.9028106927871704 -- g_loss: 1.057901382446289
Epoch: 16 -- Step: 26270 -- d_loss: 0.830070436000824 -- g_loss: 1.085157036781311
Epoch: 16 -- Step: 26280 -- d_loss: 0.8591445684432983 -- g_loss: 0.8185773491859436
Epoch: 16 -- Step: 26290 -- d_loss: 0.9144023656845093 -- g_loss: 1.034906029701233
Epoch: 16 -- Step: 26300 -- d_loss: 0.9580663442611694 -- g_loss: 0.8289994597434998
Epoch: 16 -- Step: 26310 -- d_loss: 1.4480587244033813 -- g_loss: 0.4909074008464813
Epoch: 16 -- Step: 26320 -- d_loss: 1.204048991203308 -- g_loss: 0.522038996219635
Epoch: 16 -- Step: 26330 -- d_loss: 1.5269356966018677 -- g_loss: 0.37737560272216797
Epoch: 16 -- Step: 26340 -- d_loss: 1.1433494091033936 -- g_loss: 0.6027698516845703
Epoch: 16 -- Step: 26350 -- d_loss: 1.1319259405136108 -- g_loss: 0.6289035081863403
Epoch: 16 -- Step: 26360 -- d_loss: 1.3195216655731201 -- g_loss: 0.4205554127693176
Epoch: 16 -- Step: 26370 -- d_loss: 1.0672907829284668 -- g_loss: 0.8088856935501099
Epoch: 16 -- Step: 26380 -- d_loss: 1.0762001276016235 -- g_loss: 0.7141760587692261
Epoch: 16 -- Step: 26390 -- d_loss: 1.166325330734253 -- g_loss: 0.5860678553581238
Epoch: 16 -- Step: 26400 -- d_loss: 1.0887681245803833 -- g_loss: 0.8432638645172119
Epoch: 16 -- Step: 26410 -- d_loss: 1.151763916015625 -- g_loss: 0.5479375123977661
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Epoch: 16 -- Step: 26430 -- d_loss: 0.8292878866195679 -- g_loss: 0.9265666007995605
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Epoch: 16 -- Step: 26450 -- d_loss: 0.8493834733963013 -- g_loss: 1.6361316442489624
Epoch: 16 -- Step: 26460 -- d_loss: 1.2966059446334839 -- g_loss: 0.470542848110199
Epoch: 16 -- Step: 26470 -- d_loss: 1.026490330696106 -- g_loss: 0.8628852367401123
Epoch: 16 -- Step: 26480 -- d_loss: 1.4883759021759033 -- g_loss: 0.4402874708175659
Epoch: 16 -- Step: 26490 -- d_loss: 1.0275124311447144 -- g_loss: 0.8932230472564697
Epoch: 16 -- Step: 26500 -- d_loss: 1.1465400457382202 -- g_loss: 0.7475637793540955
Epoch: 16 -- Step: 26510 -- d_loss: 1.03840172290802 -- g_loss: 0.7889426350593567
Epoch: 16 -- Step: 26520 -- d_loss: 1.174102544784546 -- g_loss: 0.5749186277389526
Epoch: 16 -- Step: 26530 -- d_loss: 1.1650742292404175 -- g_loss: 0.5684823393821716
Epoch: 16 -- Step: 26540 -- d_loss: 1.139197587966919 -- g_loss: 0.6433666944503784
Epoch: 16 -- Step: 26550 -- d_loss: 0.49777454137802124 -- g_loss: 1.884741187095642
Epoch: 16 -- Step: 26560 -- d_loss: 0.8121683597564697 -- g_loss: 1.91151762008667
Epoch: 16 -- Step: 26570 -- d_loss: 0.9661046862602234 -- g_loss: 0.7141321301460266
Epoch: 16 -- Step: 26580 -- d_loss: 1.076521873474121 -- g_loss: 1.3023496866226196
Epoch: 16 -- Step: 26590 -- d_loss: 1.0232408046722412 -- g_loss: 0.7969914674758911
Epoch: 16 -- Step: 26600 -- d_loss: 0.8435758352279663 -- g_loss: 0.7874174118041992
Epoch: 16 -- Step: 26610 -- d_loss: 0.9299304485321045 -- g_loss: 0.9532334804534912
Epoch: 16 -- Step: 26620 -- d_loss: 1.2865349054336548 -- g_loss: 0.6918333768844604
Epoch: 16 -- Step: 26630 -- d_loss: 1.0568513870239258 -- g_loss: 1.1541297435760498
Epoch: 16 -- Step: 26640 -- d_loss: 0.9690059423446655 -- g_loss: 1.1636145114898682
Epoch: 16 -- Step: 26650 -- d_loss: 0.7679618000984192 -- g_loss: 0.9558489322662354
Epoch: 16 -- Step: 26660 -- d_loss: 1.2692780494689941 -- g_loss: 0.5386494398117065
Epoch: 16 -- Step: 26670 -- d_loss: 0.8124270439147949 -- g_loss: 1.6386810541152954
Epoch: 16 -- Step: 26680 -- d_loss: 1.263780951499939 -- g_loss: 0.46104198694229126
Epoch: 16 -- Step: 26690 -- d_loss: 1.2003333568572998 -- g_loss: 1.1049489974975586
Epoch: 16 -- Step: 26700 -- d_loss: 1.066990613937378 -- g_loss: 0.6290208101272583
Epoch: 16 -- Step: 26710 -- d_loss: 1.128418207168579 -- g_loss: 0.7604244947433472
Epoch: 16 -- Step: 26720 -- d_loss: 0.9795117378234863 -- g_loss: 0.8949793577194214
Epoch: 16 -- Step: 26730 -- d_loss: 0.7893458604812622 -- g_loss: 1.1259536743164062
Epoch: 16 -- Step: 26740 -- d_loss: 0.7658581137657166 -- g_loss: 0.8659433126449585
Epoch: 16 -- Step: 26750 -- d_loss: 1.4149349927902222 -- g_loss: 0.4067462682723999
Epoch: 16 -- Step: 26760 -- d_loss: 0.9636608958244324 -- g_loss: 1.3869531154632568
Epoch: 16 -- Step: 26770 -- d_loss: 1.17167329788208 -- g_loss: 0.5752176642417908
Epoch: 16 -- Step: 26780 -- d_loss: 0.9605655670166016 -- g_loss: 0.9598818421363831
Epoch: 16 -- Step: 26790 -- d_loss: 1.240977168083191 -- g_loss: 0.5454578995704651
Epoch: 16 -- Step: 26800 -- d_loss: 0.9735231399536133 -- g_loss: 0.804394006729126
Epoch: 16 -- Step: 26810 -- d_loss: 1.1989339590072632 -- g_loss: 0.540631890296936
Epoch: 16 -- Step: 26820 -- d_loss: 1.025977373123169 -- g_loss: 0.6488333940505981
Epoch: 16 -- Step: 26830 -- d_loss: 1.2238194942474365 -- g_loss: 0.7852591276168823
Epoch: 16 -- Step: 26840 -- d_loss: 0.9350297451019287 -- g_loss: 0.8919783234596252
Epoch: 16 -- Step: 26850 -- d_loss: 0.7606116533279419 -- g_loss: 1.2046781778335571
Epoch: 16 -- Step: 26860 -- d_loss: 1.0424110889434814 -- g_loss: 0.8290156126022339
Epoch: 16 -- Step: 26870 -- d_loss: 1.0457184314727783 -- g_loss: 0.6492207050323486
Epoch: 16 -- Step: 26880 -- d_loss: 1.762007713317871 -- g_loss: 2.832287311553955
Epoch: 16 -- Step: 26890 -- d_loss: 0.914118766784668 -- g_loss: 0.8117431402206421
Epoch: 17 -- Step: 26900 -- d_loss: 0.8199107646942139 -- g_loss: 0.9006682634353638
Epoch: 17 -- Step: 26910 -- d_loss: 1.1245660781860352 -- g_loss: 0.6614976525306702
Epoch: 17 -- Step: 26920 -- d_loss: 1.5071665048599243 -- g_loss: 0.39888423681259155
Epoch: 17 -- Step: 26930 -- d_loss: 1.1747052669525146 -- g_loss: 0.6162768602371216
Epoch: 17 -- Step: 26940 -- d_loss: 1.22255539894104 -- g_loss: 0.7780635952949524
Epoch: 17 -- Step: 26950 -- d_loss: 1.4053127765655518 -- g_loss: 0.40087682008743286
Epoch: 17 -- Step: 26960 -- d_loss: 0.9080085158348083 -- g_loss: 0.7783023118972778
Epoch: 17 -- Step: 26970 -- d_loss: 0.8066152930259705 -- g_loss: 0.9441925287246704
Epoch: 17 -- Step: 26980 -- d_loss: 1.1140055656433105 -- g_loss: 0.6959748864173889
Epoch: 17 -- Step: 26990 -- d_loss: 1.0031630992889404 -- g_loss: 1.0326366424560547
Epoch: 17 -- Step: 27000 -- d_loss: 1.218328833580017 -- g_loss: 0.5652157664299011
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Epoch: 17 -- Step: 27020 -- d_loss: 1.2562763690948486 -- g_loss: 0.5615617632865906
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Epoch: 17 -- Step: 27770 -- d_loss: 1.6219751834869385 -- g_loss: 0.31145456433296204
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Epoch: 17 -- Step: 27950 -- d_loss: 1.0586285591125488 -- g_loss: 0.7900546789169312
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Epoch: 17 -- Step: 27980 -- d_loss: 0.6998240947723389 -- g_loss: 1.2868123054504395
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Epoch: 17 -- Step: 28030 -- d_loss: 1.1982682943344116 -- g_loss: 0.5497432947158813
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Epoch: 17 -- Step: 28070 -- d_loss: 1.0480248928070068 -- g_loss: 0.728646993637085
Epoch: 17 -- Step: 28080 -- d_loss: 0.8815004229545593 -- g_loss: 1.135244607925415
Epoch: 17 -- Step: 28090 -- d_loss: 1.186671257019043 -- g_loss: 0.5679270029067993
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Epoch: 17 -- Step: 28110 -- d_loss: 1.0096051692962646 -- g_loss: 1.3072617053985596
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Epoch: 17 -- Step: 28130 -- d_loss: 1.680631399154663 -- g_loss: 0.30366936326026917
Epoch: 17 -- Step: 28140 -- d_loss: 0.8319490551948547 -- g_loss: 1.4749767780303955
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Epoch: 17 -- Step: 28160 -- d_loss: 0.670892059803009 -- g_loss: 1.1857049465179443
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Epoch: 18 -- Step: 29150 -- d_loss: 0.776084303855896 -- g_loss: 1.199159860610962
Epoch: 18 -- Step: 29160 -- d_loss: 1.0616360902786255 -- g_loss: 1.1029913425445557
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Epoch: 18 -- Step: 29180 -- d_loss: 1.2713558673858643 -- g_loss: 0.47006088495254517
Epoch: 18 -- Step: 29190 -- d_loss: 0.8909873962402344 -- g_loss: 1.4875133037567139
Epoch: 18 -- Step: 29200 -- d_loss: 1.3584805727005005 -- g_loss: 0.4428101181983948
Epoch: 18 -- Step: 29210 -- d_loss: 1.0093090534210205 -- g_loss: 1.0587470531463623
Epoch: 18 -- Step: 29220 -- d_loss: 1.1333532333374023 -- g_loss: 0.8269634246826172
Epoch: 18 -- Step: 29230 -- d_loss: 0.6434774994850159 -- g_loss: 1.2936536073684692
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Epoch: 18 -- Step: 29490 -- d_loss: 1.2823879718780518 -- g_loss: 0.4806067645549774
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Epoch: 18 -- Step: 29570 -- d_loss: 0.9309190511703491 -- g_loss: 0.9321858882904053
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Epoch: 18 -- Step: 29590 -- d_loss: 1.4673311710357666 -- g_loss: 0.40863871574401855
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Epoch: 18 -- Step: 29610 -- d_loss: 0.8244689702987671 -- g_loss: 0.9486010074615479
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Epoch: 18 -- Step: 29630 -- d_loss: 0.7897823452949524 -- g_loss: 0.9703947305679321
Epoch: 18 -- Step: 29640 -- d_loss: 1.1911368370056152 -- g_loss: 0.5774309635162354
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Epoch: 18 -- Step: 29660 -- d_loss: 1.0652616024017334 -- g_loss: 0.6744453310966492
Epoch: 18 -- Step: 29670 -- d_loss: 1.049843192100525 -- g_loss: 0.7021495699882507
Epoch: 18 -- Step: 29680 -- d_loss: 1.0944557189941406 -- g_loss: 0.6055033206939697
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Epoch: 18 -- Step: 29730 -- d_loss: 0.9999386072158813 -- g_loss: 0.903170645236969
Epoch: 18 -- Step: 29740 -- d_loss: 1.1544485092163086 -- g_loss: 1.8937134742736816
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Epoch: 18 -- Step: 29760 -- d_loss: 1.447563886642456 -- g_loss: 0.41195589303970337
Epoch: 18 -- Step: 29770 -- d_loss: 1.4983172416687012 -- g_loss: 0.38374781608581543
Epoch: 18 -- Step: 29780 -- d_loss: 0.7972865104675293 -- g_loss: 0.9220156073570251
Epoch: 18 -- Step: 29790 -- d_loss: 1.2366126775741577 -- g_loss: 0.5244291424751282
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Epoch: 18 -- Step: 29810 -- d_loss: 1.0229722261428833 -- g_loss: 0.987236738204956
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Epoch: 18 -- Step: 29940 -- d_loss: 0.7851567268371582 -- g_loss: 0.8549400568008423
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Epoch: 18 -- Step: 29990 -- d_loss: 1.1375609636306763 -- g_loss: 0.9697721600532532
Epoch: 18 -- Step: 30000 -- d_loss: 1.0942121744155884 -- g_loss: 0.658065915107727
Epoch: 18 -- Step: 30010 -- d_loss: 1.6222960948944092 -- g_loss: 0.33017557859420776
Epoch: 18 -- Step: 30020 -- d_loss: 1.2245997190475464 -- g_loss: 0.6800923347473145
Epoch: 18 -- Step: 30030 -- d_loss: 0.7801588773727417 -- g_loss: 1.2721872329711914
Epoch: 18 -- Step: 30040 -- d_loss: 0.7545886039733887 -- g_loss: 1.0949280261993408
Epoch: 18 -- Step: 30050 -- d_loss: 1.014897108078003 -- g_loss: 0.7425205707550049
Epoch: 19 -- Step: 30060 -- d_loss: 1.095610499382019 -- g_loss: 0.6012181043624878
Epoch: 19 -- Step: 30070 -- d_loss: 1.1284732818603516 -- g_loss: 0.7168375849723816
Epoch: 19 -- Step: 30080 -- d_loss: 0.7608145475387573 -- g_loss: 1.1248562335968018
Epoch: 19 -- Step: 30090 -- d_loss: 1.074550986289978 -- g_loss: 0.6591218709945679
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Epoch: 19 -- Step: 31200 -- d_loss: 1.279861569404602 -- g_loss: 0.5369360446929932
Epoch: 19 -- Step: 31210 -- d_loss: 0.7029823660850525 -- g_loss: 1.1793148517608643
Epoch: 19 -- Step: 31220 -- d_loss: 1.1455910205841064 -- g_loss: 0.5416557788848877
Epoch: 19 -- Step: 31230 -- d_loss: 1.285020112991333 -- g_loss: 0.46809282898902893
Epoch: 19 -- Step: 31240 -- d_loss: 1.2515170574188232 -- g_loss: 0.49971896409988403
Epoch: 19 -- Step: 31250 -- d_loss: 0.7883325219154358 -- g_loss: 1.0072897672653198
Epoch: 19 -- Step: 31260 -- d_loss: 1.0662455558776855 -- g_loss: 1.6368958950042725
Epoch: 19 -- Step: 31270 -- d_loss: 1.3618319034576416 -- g_loss: 0.4450606107711792
Epoch: 19 -- Step: 31280 -- d_loss: 0.864226222038269 -- g_loss: 0.8895485997200012
Epoch: 19 -- Step: 31290 -- d_loss: 0.8816205263137817 -- g_loss: 0.8394941091537476
Epoch: 19 -- Step: 31300 -- d_loss: 1.0276730060577393 -- g_loss: 0.7659559845924377
Epoch: 19 -- Step: 31310 -- d_loss: 0.9207336902618408 -- g_loss: 1.2490744590759277
Epoch: 19 -- Step: 31320 -- d_loss: 1.2726433277130127 -- g_loss: 0.5049201250076294
Epoch: 19 -- Step: 31330 -- d_loss: 1.0692529678344727 -- g_loss: 0.6452456116676331
Epoch: 19 -- Step: 31340 -- d_loss: 0.8201664686203003 -- g_loss: 0.9821822643280029
Epoch: 19 -- Step: 31350 -- d_loss: 1.1778345108032227 -- g_loss: 0.6171993613243103
Epoch: 19 -- Step: 31360 -- d_loss: 0.9410091042518616 -- g_loss: 0.7149861454963684
Epoch: 19 -- Step: 31370 -- d_loss: 1.0495260953903198 -- g_loss: 0.8843044638633728
Epoch: 19 -- Step: 31380 -- d_loss: 1.3296501636505127 -- g_loss: 0.44968119263648987
Epoch: 19 -- Step: 31390 -- d_loss: 1.0093899965286255 -- g_loss: 0.7678624391555786
Epoch: 19 -- Step: 31400 -- d_loss: 1.0771844387054443 -- g_loss: 0.6357223391532898
Epoch: 19 -- Step: 31410 -- d_loss: 0.9190840721130371 -- g_loss: 1.0156714916229248
Epoch: 19 -- Step: 31420 -- d_loss: 1.0696382522583008 -- g_loss: 0.9874459505081177
Epoch: 19 -- Step: 31430 -- d_loss: 0.9156660437583923 -- g_loss: 1.1460163593292236
Epoch: 19 -- Step: 31440 -- d_loss: 1.3221935033798218 -- g_loss: 0.48195189237594604
Epoch: 19 -- Step: 31450 -- d_loss: 1.2032725811004639 -- g_loss: 0.5315960645675659
Epoch: 19 -- Step: 31460 -- d_loss: 0.8936551809310913 -- g_loss: 1.409350872039795
Epoch: 19 -- Step: 31470 -- d_loss: 0.7267187833786011 -- g_loss: 1.1944221258163452
Epoch: 19 -- Step: 31480 -- d_loss: 0.8290855884552002 -- g_loss: 0.9786823391914368
Epoch: 19 -- Step: 31490 -- d_loss: 1.5071938037872314 -- g_loss: 0.3492473363876343
Epoch: 19 -- Step: 31500 -- d_loss: 0.847723662853241 -- g_loss: 1.1840665340423584
Epoch: 19 -- Step: 31510 -- d_loss: 0.8285590410232544 -- g_loss: 0.8786795139312744
Epoch: 19 -- Step: 31520 -- d_loss: 0.8073294162750244 -- g_loss: 0.9948317408561707
Epoch: 19 -- Step: 31530 -- d_loss: 1.122805118560791 -- g_loss: 0.5898324251174927
Epoch: 19 -- Step: 31540 -- d_loss: 1.0461024045944214 -- g_loss: 0.6499029994010925
Epoch: 19 -- Step: 31550 -- d_loss: 0.9889886379241943 -- g_loss: 0.8837471008300781
Epoch: 19 -- Step: 31560 -- d_loss: 0.8612461090087891 -- g_loss: 1.0388143062591553
Epoch: 19 -- Step: 31570 -- d_loss: 0.7741495370864868 -- g_loss: 0.9470747709274292
Epoch: 19 -- Step: 31580 -- d_loss: 0.9336786270141602 -- g_loss: 0.7748023271560669
Epoch: 19 -- Step: 31590 -- d_loss: 0.7514257431030273 -- g_loss: 1.1701009273529053
Epoch: 19 -- Step: 31600 -- d_loss: 1.1196757555007935 -- g_loss: 0.6376916170120239
Epoch: 19 -- Step: 31610 -- d_loss: 1.0578854084014893 -- g_loss: 0.6767321825027466
Epoch: 19 -- Step: 31620 -- d_loss: 1.1476221084594727 -- g_loss: 0.7038830518722534
Epoch: 19 -- Step: 31630 -- d_loss: 1.267543911933899 -- g_loss: 0.5084195137023926
Epoch: 19 -- Step: 31640 -- d_loss: 0.7961710095405579 -- g_loss: 1.24009108543396

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.